negative findings in research

Positives in negative results: when finding ‘nothing’ means something

Doctoral Candidate, University of Wollongong

Associate Research Fellow, CNS disorders, University of Wollongong

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UNDERSTANDING RESEARCH: What do we actually mean by research and how does it help inform our understanding of things? It’s important to publish all results – both positive and negative – if researchers are to avoid repeating old mistakes. But where is the glory in negative results?

Scientists usually communicate their latest findings by publishing results as scientific papers in journals that are almost always accessible online (albeit often at a price), ensuring fast sharing of latest knowledge.

But negative findings – those that do not agree with what the researchers hypothesised – are often overlooked, discouraged or simply not put forward for publication.

Yet negative findings can save scientists valuable time and resources by not repeating already performed experiments, so it is important that all results, regardless of the outcome, are published.

Adding human nature to the mix

Despite devoting their lives to logic and facts, scientists are still human. Their decisions are influenced by emotions and opinions. They are, at times, unlikely to trust conflicting results due to a pre-existing belief that something else is true.

This phenomenon is known as cognitive bias . If presented with evidence that disproves an old theory, scientists may simply attribute the discrepancy to experimental error.

In extreme cases, reporting a negative result, particularly when it refutes previous research, is to some extent considered a form of discreditation.

At other times, human error and the fact that science cannot always be reproduced has led to the belief that negative results are associated with flawed or poor science.

Revolt against the negative-finding culture

The stigma surrounding negative findings means that they are a low priority for publication. High-quality journals are less likely to accept negative findings because they are associated with a lower citation rate, lower impact knowledge and are often controversial.

This raises a major issue: if results are not reported (positive or negative) then other scientists may waste time and resources needlessly repeating experiments.

Or, in some situations, theories that are untrue or incomplete are never corrected, despite their potentially dire consequences (as in the case of the measles, mumps and rubella MMR vaccine despite the original research linking it to autism being retracted by The Lancet ).

A scientist’s success depends largely on the impact of their research. Higher-impact findings published in prominent journals tend to attract more funding grants.

As citations are a measure of a scientist’s worth, and negative results attract fewer citations , many scientists simply choose not to spend the time publishing negative results.

Dissemination of negative results has traditionally been one of the hardest battles faced by scientists. It is particularly difficult when these negative findings contradict previously published research, even though many reputable journals have policies to publish such work.

It was a problem Australian researcher David Vaux wrote about in a Retraction Watch blog on his attempts to publish contradictory results.

In recent years, open-access and broad-scope journals such as PLOS One , Frontiers and the Biomed Central journal series are increasingly publishing papers with negative findings.

Additionally, a number of journals have surfaced whose primary objective is to disseminate negative findings, such as Journal of Articles in Support of the Null Hypothesis , Journal of Negative Results in Biomedicine and The All Results Journal .

The purpose of these journals is to give negative findings a home, where they can still be accessed widely by the international science community without facing prejudice in the review process.

But these journals have lower publication rates, reflective of a scientific culture that deems negative results less valuable.

How to turn a negative into a positive

The issues surrounding the negative finding culture are certainly gaining traction. Many reputable journals such as Disease Models & Mechanisms and Nature have covered the topic recently.

Nonetheless, publication bias is still an issue, indicating that a shift in the scientific culture is required.

Some journals have suggested that negative findings be published open access and free of charge, while others have suggested that scientists be encouraged to submit corrections as well as new results.

Additionally, a push by funding agencies for scientists to make available all data gathered (such as via Open Science ) from their support may reduce the stigma attached to negative findings.

As proposed by American physicist and philosopher Thomas Kuhn , a shift in scientific thinking will occur when the amount of evidence in support of the new paradigm overtakes the old one.

Following this logic, perhaps the answer to reversing the anti-negative-finding culture lies in educating young scientists about the importance of disseminating all results.

This way, the next generation of scientists may experience improved scientific communication and more efficient science.

This article is part of a series on Understanding Research .

Further reading: Why research beats anecdote in our search for knowledge Clearing up confusion between correlation and causation Where’s the proof in science? There is none The risks of blowing your own trumpet too soon on research How to find the knowns and unknowns in any research How myths and tabloids feed on anomalies in science The 10 stuff-ups we all make when interpreting research

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How to Write About Negative (Or Null) Results in Academic Research

Researchers are often disappointed when their work yields "negative" results, meaning that the null hypothesis cannot be rejected. However, negative results are essential for research to progress. Negative results tell researchers that they are on the wrong path, or that their current techniques are ineffective. This is a natural and necessary part of discovering something that was previously unknown. Solving problems that lead to negative results is an integral part of being an effective researcher. Publishing negative results that are the result of rigorous research contributes to scientific progress.

There are three main reasons for negative results:

• The original hypothesis was incorrect
• The findings of a published report cannot be replicated
• Technical problems

Here, we will discuss how to write about negative results, first focusing on the most common reason: technical problems.

Writing about technical problems

Technical problems might include faulty reagents, inappropriate study design, and insufficient statistical power. Most researchers would prefer to resolve technical problems before presenting their work, and focus instead on their convincing results. In reality, researchers often need to present their work at a conference or to a thesis committee before some problems can be resolved.

When presenting at a conference, the objective should be to clearly describe your overall research goal and why it is important, your preliminary results, the current problem, and how previously published work is informing the steps you are taking to resolve the problem. Here, you want to take advantage of the collective expertise at the conference. By being straightforward about your difficulties, you increase the chance that someone can help you find a solution.

When presenting to a thesis committee, much of what you discuss will be the same (overall research goal and why it is important, results, problem(s) and possible solutions). Your primarily goal is to show that you are well prepared to move forward in your research career, despite the recent difficulties. The thesis defense is a defined stopping point, so most thesis students should write about solutions they would pursue if they were to continue the work. For example, "To resolve this problem, it would be advisable to increase the survey area by a factor of 4, and then…" In contrast, researchers who will be continuing their work should write about possible solutions using present and future tense. For example, "To resolve this problem, we are currently testing a wider variety of standards, and will then conduct preliminary experiments to determine…"

Putting the "re" in "research"

Whether you are presenting at a conference, defending a thesis, applying for funding, or simply trying to make progress in your research, you will often need to search through the academic literature to determine the best path forward. This is especially true when you get unexpected results—either positive or negative. When trying to resolve a technical problem, you should often find yourself carefully reading the materials and methods sections of papers that address similar research questions, or that used similar techniques to explore very different problems. For example, a single computer algorithm might be adapted to address research questions in many different fields.

In searching through published papers and less formal methods of communication—such as conference abstracts—you may come to appreciate the important details that good researchers will include when discussing technical problems or other negative results. For example, "We found that participants were more likely to complete the process when light refreshments were provided between the two sessions." By including this information, the authors may help other researchers save time and resources.

Thus, you are advised to be as thorough as possible in reviewing the relevant literature, to find the most promising solutions for technical problems. When presenting your work, show that you have carefully considered the possibilities, and have developed a realistic plan for moving forward. This will help a thesis committee view your efforts favorably, and can also convince possible collaborators or advisors to invest time in helping you.

Publishing negative results

Negative results due to technical problems may be acceptable for a conference presentation or a thesis at the undergraduate or master's degree level. Negative results due to technical problems are not sufficient for publication, a Ph.D. dissertation, or tenure. In those situations, you will need to resolve the technical problem and generate high quality results (either positive or negative) that stand up to rigorous analysis. Depending on the research field, high quality negative results might include multiple readouts and narrow confidence intervals.

Researchers are often reluctant to publish negative results, especially if their data don't support an interesting alternative hypothesis. Traditionally, journals have been reluctant to publish negative results that are not paired with positive results, even if the study is well designed and the results have sufficient statistical power. This is starting to change— especially for medical research —but publishing negative results can still be an uphill battle.

Not publishing high quality negative results is a disservice to the scientific community and the people who support it (including tax payers), since other scientists may need to repeat the work. For studies involving animal research or human tissue samples, not publishing would squander significant sacrifices. For research involving medical treatments—especially studies that contradict a published report—not publishing negative results leads to an inaccurate understanding of treatment efficacy.

So how can researchers write about negative results in a way that reflects its importance? Let's consider a common reason for negative results: the original hypothesis was incorrect.

Writing about negative results when the original hypothesis was incorrect

Researchers should be comfortable with being wrong some of the time, such as when results don't support an initial hypothesis. After all, research wouldn't be necessary if we already knew the answer to every possible question. The next step is usually to revise the hypothesis after reconsidering the available data, reading through the relevant literature, and consulting with colleagues.

Ideally, a revised hypothesis will lead to results that allow you to reject a (revised) null hypothesis. The negative results can then be reported alongside the positive results, possibly bolstering the significance of both. For example, "The DNA mutations in region A had a significant effect on gene expression, while the mutations outside of domain A had no effect. Don't forget to include important details about how you overcame technical problems, so that other researchers don't need to reinvent the wheel.

Unfortunately, it isn't always possible to pair negative results with related positive results. For example, imagine a year-long study on the effect of COVID-19 shelter-in-place orders on the mental health of avid video game players compared to people who don't play video games. Despite using well-established tools for measuring mental health, having a large sample size, and comparing multiple subpopulations (e.g. gamers who live alone vs. gamers who live with others), no significant differences were identified. There is no way to modify and repeat this study because the same shelter-in-place conditions no longer exist. So how can this research be presented effectively?

Writing when you only have negative results

When you write a scientific paper to report negative results, the sections will be the same as for any other paper: Introduction, Materials and Methods, Results and Discussion. In the introduction, you should prepare your reader for the possibility of negative results. You can highlight gaps or inconsistencies in past research, and point to data that could indicate an incomplete understanding of the situation.

In the example about video game players, you might highlight data showing that gamers are statistically very similar to large chunks of the population in terms of age, education, marital status, etc. You might discuss how the stigma associated with playing video games might be unfair and harmful to people in certain situations. You could discuss research showing the benefits of playing video games, and contrast gaming with engaging in social media, which is another modern hobby. Putting a positive spin on negative results can make the difference between a published manuscript and rejection.

In a paper that focuses on negative results—especially one that contradicts published findings—the research design and data analysis must be impeccable. You may need to collaborate with other researchers to ensure that your methods are sound, and apply multiple methods of data analysis.

As long as the research is rigorous, negative results should be used to inform and guide future experiments. This is how science improves our understanding of the world.

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Types of Bias in Research | Definition & Examples

Research bias results from any deviation from the truth, causing distorted results and wrong conclusions. Bias can occur at any phase of your research, including during data collection , data analysis , interpretation, or publication. Research bias can occur in both qualitative and quantitative research .

Understanding research bias is important for several reasons.

• Bias exists in all research, across research designs , and is difficult to eliminate.
• Bias can occur at any stage of the research process .
• Bias impacts the validity and reliability of your findings, leading to misinterpretation of data.

It is almost impossible to conduct a study without some degree of research bias. It’s crucial for you to be aware of the potential types of bias, so you can minimize them.

For example, the success rate of the program will likely be affected if participants start to drop out ( attrition ). Participants who become disillusioned due to not losing weight may drop out, while those who succeed in losing weight are more likely to continue. This in turn may bias the findings towards more favorable results.  

Table of contents

Information bias, interviewer bias.

• Publication bias

Researcher bias

Response bias.

Selection bias

Cognitive bias

How to avoid bias in research

Other types of research bias, frequently asked questions about research bias.

Information bias , also called measurement bias, arises when key study variables are inaccurately measured or classified. Information bias occurs during the data collection step and is common in research studies that involve self-reporting and retrospective data collection. It can also result from poor interviewing techniques or differing levels of recall from participants.

The main types of information bias are:

• Recall bias
• Observer bias

Performance bias

Regression to the mean (rtm).

Over a period of four weeks, you ask students to keep a journal, noting how much time they spent on their smartphones along with any symptoms like muscle twitches, aches, or fatigue.

Recall bias is a type of information bias. It occurs when respondents are asked to recall events in the past and is common in studies that involve self-reporting.

As a rule of thumb, infrequent events (e.g., buying a house or a car) will be memorable for longer periods of time than routine events (e.g., daily use of public transportation). You can reduce recall bias by running a pilot survey and carefully testing recall periods. If possible, test both shorter and longer periods, checking for differences in recall.

• A group of children who have been diagnosed, called the case group
• A group of children who have not been diagnosed, called the control group

Since the parents are being asked to recall what their children generally ate over a period of several years, there is high potential for recall bias in the case group.

The best way to reduce recall bias is by ensuring your control group will have similar levels of recall bias to your case group. Parents of children who have childhood cancer, which is a serious health problem, are likely to be quite concerned about what may have contributed to the cancer.

Thus, if asked by researchers, these parents are likely to think very hard about what their child ate or did not eat in their first years of life. Parents of children with other serious health problems (aside from cancer) are also likely to be quite concerned about any diet-related question that researchers ask about.

Observer bias is the tendency of research participants to see what they expect or want to see, rather than what is actually occurring. Observer bias can affect the results in observationa l and experimental studies, where subjective judgment (such as assessing a medical image) or measurement (such as rounding blood pressure readings up or down) is part of the d ata collection process.

Observer bias leads to over- or underestimation of true values, which in turn compromise the validity of your findings. You can reduce observer bias by using double-blinded  and single-blinded research methods.

Based on discussions you had with other researchers before starting your observations , you are inclined to think that medical staff tend to simply call each other when they need specific patient details or have questions about treatments.

At the end of the observation period, you compare notes with your colleague. Your conclusion was that medical staff tend to favor phone calls when seeking information, while your colleague noted down that medical staff mostly rely on face-to-face discussions. Seeing that your expectations may have influenced your observations, you and your colleague decide to conduct semi-structured interviews with medical staff to clarify the observed events. Note: Observer bias and actor–observer bias are not the same thing.

Performance bias is unequal care between study groups. Performance bias occurs mainly in medical research experiments, if participants have knowledge of the planned intervention, therapy, or drug trial before it begins.

Studies about nutrition, exercise outcomes, or surgical interventions are very susceptible to this type of bias. It can be minimized by using blinding , which prevents participants and/or researchers from knowing who is in the control or treatment groups. If blinding is not possible, then using objective outcomes (such as hospital admission data) is the best approach.

When the subjects of an experimental study change or improve their behavior because they are aware they are being studied, this is called the Hawthorne effect (or observer effect). Similarly, the John Henry effect occurs when members of a control group are aware they are being compared to the experimental group. This causes them to alter their behavior in an effort to compensate for their perceived disadvantage.

Regression to the mean (RTM) is a statistical phenomenon that refers to the fact that a variable that shows an extreme value on its first measurement will tend to be closer to the center of its distribution on a second measurement.

Medical research is particularly sensitive to RTM. Here, interventions aimed at a group or a characteristic that is very different from the average (e.g., people with high blood pressure) will appear to be successful because of the regression to the mean. This can lead researchers to misinterpret results, describing a specific intervention as causal when the change in the extreme groups would have happened anyway.

In general, among people with depression, certain physical and mental characteristics have been observed to deviate from the population mean .

This could lead you to think that the intervention was effective when those treated showed improvement on measured post-treatment indicators, such as reduced severity of depressive episodes.

However, given that such characteristics deviate more from the population mean in people with depression than in people without depression, this improvement could be attributed to RTM.

Interviewer bias stems from the person conducting the research study. It can result from the way they ask questions or react to responses, but also from any aspect of their identity, such as their sex, ethnicity, social class, or perceived attractiveness.

Interviewer bias distorts responses, especially when the characteristics relate in some way to the research topic. Interviewer bias can also affect the interviewer’s ability to establish rapport with the interviewees, causing them to feel less comfortable giving their honest opinions about sensitive or personal topics.

Participant: “I like to solve puzzles, or sometimes do some gardening.”

You: “I love gardening, too!”

In this case, seeing your enthusiastic reaction could lead the participant to talk more about gardening.

Establishing trust between you and your interviewees is crucial in order to ensure that they feel comfortable opening up and revealing their true thoughts and feelings. At the same time, being overly empathetic can influence the responses of your interviewees, as seen above.

Publication bias occurs when the decision to publish research findings is based on their nature or the direction of their results. Studies reporting results that are perceived as positive, statistically significant , or favoring the study hypotheses are more likely to be published due to publication bias.

Publication bias is related to data dredging (also called p -hacking ), where statistical tests on a set of data are run until something statistically significant happens. As academic journals tend to prefer publishing statistically significant results, this can pressure researchers to only submit statistically significant results. P -hacking can also involve excluding participants or stopping data collection once a p value of 0.05 is reached. However, this leads to false positive results and an overrepresentation of positive results in published academic literature.

Researcher bias occurs when the researcher’s beliefs or expectations influence the research design or data collection process. Researcher bias can be deliberate (such as claiming that an intervention worked even if it didn’t) or unconscious (such as letting personal feelings, stereotypes, or assumptions influence research questions ).

The unconscious form of researcher bias is associated with the Pygmalion effect (or Rosenthal effect ), where the researcher’s high expectations (e.g., that patients assigned to a treatment group will succeed) lead to better performance and better outcomes.

Researcher bias is also sometimes called experimenter bias, but it applies to all types of investigative projects, rather than only to experimental designs .

• Good question: What are your views on alcohol consumption among your peers?
• Bad question: Do you think it’s okay for young people to drink so much?

Response bias is a general term used to describe a number of different situations where respondents tend to provide inaccurate or false answers to self-report questions, such as those asked on surveys or in structured interviews .

This happens because when people are asked a question (e.g., during an interview ), they integrate multiple sources of information to generate their responses. Because of that, any aspect of a research study may potentially bias a respondent. Examples include the phrasing of questions in surveys, how participants perceive the researcher, or the desire of the participant to please the researcher and to provide socially desirable responses.

Response bias also occurs in experimental medical research. When outcomes are based on patients’ reports, a placebo effect can occur. Here, patients report an improvement despite having received a placebo, not an active medical treatment.

While interviewing a student, you ask them:

“Do you think it’s okay to cheat on an exam?”

Common types of response bias are:

Acquiescence bias

Demand characteristics.

• Social desirability bias

Courtesy bias

• Question-order bias

Extreme responding

Acquiescence bias is the tendency of respondents to agree with a statement when faced with binary response options like “agree/disagree,” “yes/no,” or “true/false.” Acquiescence is sometimes referred to as “yea-saying.”

This type of bias occurs either due to the participant’s personality (i.e., some people are more likely to agree with statements than disagree, regardless of their content) or because participants perceive the researcher as an expert and are more inclined to agree with the statements presented to them.

Q: Are you a social person?

People who are inclined to agree with statements presented to them are at risk of selecting the first option, even if it isn’t fully supported by their lived experiences.

In order to control for acquiescence, consider tweaking your phrasing to encourage respondents to make a choice truly based on their preferences. Here’s an example:

Q: What would you prefer?

• A quiet night in
• A night out with friends

Demand characteristics are cues that could reveal the research agenda to participants, risking a change in their behaviors or views. Ensuring that participants are not aware of the research objectives is the best way to avoid this type of bias.

On each occasion, patients reported their pain as being less than prior to the operation. While at face value this seems to suggest that the operation does indeed lead to less pain, there is a demand characteristic at play. During the interviews, the researcher would unconsciously frown whenever patients reported more post-op pain. This increased the risk of patients figuring out that the researcher was hoping that the operation would have an advantageous effect.

Social desirability bias is the tendency of participants to give responses that they believe will be viewed favorably by the researcher or other participants. It often affects studies that focus on sensitive topics, such as alcohol consumption or sexual behavior.

You are conducting face-to-face semi-structured interviews with a number of employees from different departments. When asked whether they would be interested in a smoking cessation program, there was widespread enthusiasm for the idea.

Note that while social desirability and demand characteristics may sound similar, there is a key difference between them. Social desirability is about conforming to social norms, while demand characteristics revolve around the purpose of the research.

Courtesy bias stems from a reluctance to give negative feedback, so as to be polite to the person asking the question. Small-group interviewing where participants relate in some way to each other (e.g., a student, a teacher, and a dean) is especially prone to this type of bias.

Question order bias

Question order bias occurs when the order in which interview questions are asked influences the way the respondent interprets and evaluates them. This occurs especially when previous questions provide context for subsequent questions.

When answering subsequent questions, respondents may orient their answers to previous questions (called a halo effect ), which can lead to systematic distortion of the responses.

Extreme responding is the tendency of a respondent to answer in the extreme, choosing the lowest or highest response available, even if that is not their true opinion. Extreme responding is common in surveys using Likert scales , and it distorts people’s true attitudes and opinions.

Disposition towards the survey can be a source of extreme responding, as well as cultural components. For example, people coming from collectivist cultures tend to exhibit extreme responses in terms of agreement, while respondents indifferent to the questions asked may exhibit extreme responses in terms of disagreement.

Selection bias is a general term describing situations where bias is introduced into the research from factors affecting the study population.

Common types of selection bias are:

Sampling or ascertainment bias

• Attrition bias
• Self-selection (or volunteer) bias
• Survivorship bias
• Nonresponse bias
• Undercoverage bias

Sampling bias occurs when your sample (the individuals, groups, or data you obtain for your research) is selected in a way that is not representative of the population you are analyzing. Sampling bias threatens the external validity of your findings and influences the generalizability of your results.

The easiest way to prevent sampling bias is to use a probability sampling method . This way, each member of the population you are studying has an equal chance of being included in your sample.

Sampling bias is often referred to as ascertainment bias in the medical field.

Attrition bias occurs when participants who drop out of a study systematically differ from those who remain in the study. Attrition bias is especially problematic in randomized controlled trials for medical research because participants who do not like the experience or have unwanted side effects can drop out and affect your results.

You can minimize attrition bias by offering incentives for participants to complete the study (e.g., a gift card if they successfully attend every session). It’s also a good practice to recruit more participants than you need, or minimize the number of follow-up sessions or questions.

You provide a treatment group with weekly one-hour sessions over a two-month period, while a control group attends sessions on an unrelated topic. You complete five waves of data collection to compare outcomes: a pretest survey, three surveys during the program, and a posttest survey.

Self-selection or volunteer bias

Self-selection bias (also called volunteer bias ) occurs when individuals who volunteer for a study have particular characteristics that matter for the purposes of the study.

Volunteer bias leads to biased data, as the respondents who choose to participate will not represent your entire target population. You can avoid this type of bias by using random assignment —i.e., placing participants in a control group or a treatment group after they have volunteered to participate in the study.

Closely related to volunteer bias is nonresponse bias , which occurs when a research subject declines to participate in a particular study or drops out before the study’s completion.

Considering that the hospital is located in an affluent part of the city, volunteers are more likely to have a higher socioeconomic standing, higher education, and better nutrition than the general population.

Survivorship bias occurs when you do not evaluate your data set in its entirety: for example, by only analyzing the patients who survived a clinical trial.

This strongly increases the likelihood that you draw (incorrect) conclusions based upon those who have passed some sort of selection process—focusing on “survivors” and forgetting those who went through a similar process and did not survive.

Note that “survival” does not always mean that participants died! Rather, it signifies that participants did not successfully complete the intervention.

However, most college dropouts do not become billionaires. In fact, there are many more aspiring entrepreneurs who dropped out of college to start companies and failed than succeeded.

Nonresponse bias occurs when those who do not respond to a survey or research project are different from those who do in ways that are critical to the goals of the research. This is very common in survey research, when participants are unable or unwilling to participate due to factors like lack of the necessary skills, lack of time, or guilt or shame related to the topic.

You can mitigate nonresponse bias by offering the survey in different formats (e.g., an online survey, but also a paper version sent via post), ensuring confidentiality , and sending them reminders to complete the survey.

You notice that your surveys were conducted during business hours, when the working-age residents were less likely to be home.

Undercoverage bias occurs when you only sample from a subset of the population you are interested in. Online surveys can be particularly susceptible to undercoverage bias. Despite being more cost-effective than other methods, they can introduce undercoverage bias as a result of excluding people who do not use the internet.

Cognitive bias refers to a set of predictable (i.e., nonrandom) errors in thinking that arise from our limited ability to process information objectively. Rather, our judgment is influenced by our values, memories, and other personal traits. These create “ mental shortcuts” that help us process information intuitively and decide faster. However, cognitive bias can also cause us to misunderstand or misinterpret situations, information, or other people.

Because of cognitive bias, people often perceive events to be more predictable after they happen.

Although there is no general agreement on how many types of cognitive bias exist, some common types are:

• Anchoring bias  
• Framing effect  
• Actor-observer bias
• Availability heuristic (or availability bias)
• Confirmation bias  
• Halo effect
• The Baader-Meinhof phenomenon  

Anchoring bias

Anchoring bias is people’s tendency to fixate on the first piece of information they receive, especially when it concerns numbers. This piece of information becomes a reference point or anchor. Because of that, people base all subsequent decisions on this anchor. For example, initial offers have a stronger influence on the outcome of negotiations than subsequent ones.

• Framing effect

Framing effect refers to our tendency to decide based on how the information about the decision is presented to us. In other words, our response depends on whether the option is presented in a negative or positive light, e.g., gain or loss, reward or punishment, etc. This means that the same information can be more or less attractive depending on the wording or what features are highlighted.

Actor–observer bias

Actor–observer bias occurs when you attribute the behavior of others to internal factors, like skill or personality, but attribute your own behavior to external or situational factors.

In other words, when you are the actor in a situation, you are more likely to link events to external factors, such as your surroundings or environment. However, when you are observing the behavior of others, you are more likely to associate behavior with their personality, nature, or temperament.

One interviewee recalls a morning when it was raining heavily. They were rushing to drop off their kids at school in order to get to work on time. As they were driving down the highway, another car cut them off as they were trying to merge. They tell you how frustrated they felt and exclaim that the other driver must have been a very rude person.

At another point, the same interviewee recalls that they did something similar: accidentally cutting off another driver while trying to take the correct exit. However, this time, the interviewee claimed that they always drive very carefully, blaming their mistake on poor visibility due to the rain.

• Availability heuristic

Availability heuristic (or availability bias) describes the tendency to evaluate a topic using the information we can quickly recall to our mind, i.e., that is available to us. However, this is not necessarily the best information, rather it’s the most vivid or recent. Even so, due to this mental shortcut, we tend to think that what we can recall must be right and ignore any other information.

• Confirmation bias

Confirmation bias is the tendency to seek out information in a way that supports our existing beliefs while also rejecting any information that contradicts those beliefs. Confirmation bias is often unintentional but still results in skewed results and poor decision-making.

Let’s say you grew up with a parent in the military. Chances are that you have a lot of complex emotions around overseas deployments. This can lead you to over-emphasize findings that “prove” that your lived experience is the case for most families, neglecting other explanations and experiences.

The halo effect refers to situations whereby our general impression about a person, a brand, or a product is shaped by a single trait. It happens, for instance, when we automatically make positive assumptions about people based on something positive we notice, while in reality, we know little about them.

The Baader-Meinhof phenomenon

The Baader-Meinhof phenomenon (or frequency illusion) occurs when something that you recently learned seems to appear “everywhere” soon after it was first brought to your attention. However, this is not the case. What has increased is your awareness of something, such as a new word or an old song you never knew existed, not their frequency.

While very difficult to eliminate entirely, research bias can be mitigated through proper study design and implementation. Here are some tips to keep in mind as you get started.

• Clearly explain in your methodology section how your research design will help you meet the research objectives and why this is the most appropriate research design.
• In quantitative studies , make sure that you use probability sampling to select the participants. If you’re running an experiment, make sure you use random assignment to assign your control and treatment groups.
• Account for participants who withdraw or are lost to follow-up during the study. If they are withdrawing for a particular reason, it could bias your results. This applies especially to longer-term or longitudinal studies .
• Use triangulation to enhance the validity and credibility of your findings.
• Phrase your survey or interview questions in a neutral, non-judgmental tone. Be very careful that your questions do not steer your participants in any particular direction.
• Consider using a reflexive journal. Here, you can log the details of each interview , paying special attention to any influence you may have had on participants. You can include these in your final analysis.
• Baader–Meinhof phenomenon
• Sampling bias
• Ascertainment bias
• Self-selection bias
• Hawthorne effect
• Omitted variable bias
• Pygmalion effect
• Placebo effect

Research bias affects the validity and reliability of your research findings , leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.

Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behavior and external factors (difficult circumstances) to justify the same behavior in themselves.

Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews. These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.

Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen because people are either not willing or not able to participate.

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Seeing the Positives in Negative Results: Why it is Important to Publish ‘Negative’ Research Findings

• December 13, 2018
• Jemimah Kim
• News , Read

The weekly news cycle is routinely scattered with announcements of new scientific research that promises to change our understanding of health and disease. Unsurprisingly, the research that ends up as news headlines tends to be the more exciting information–scientists located a new gene for Alzheimer’s, researchers cloned an animal, et cetera. What often gets left out of the popular media and even from scientific journals, however, is some less exciting, but equally important research–negative findings, or null findings.

Negative findings refer to results that do not support the researcher’s initial hypothesis.[1]  Usually , a researcher predicts that their results will support the alternative hypothesis–that there  is  a difference between the two variables they are investigating–rather than the null hypothesis–that there is no difference between the variables. These results are often called non-significant, because the researcher did not find a statistically significant difference between the control group and the intervention group.[2] 

Despite the importance of negative findings for advancing research, ‘negative’ research results are significantly less likely to be published than positive results.[2] There are several reasons that negative findings are not published as often as positive findings. In the research world, there is a negative value or  stigma  attached to null findings, and there is a positive value attached to significant findings. We place a cultural value on getting positive results and correctly proving your original hypothesis. The very naming of the terms themselves– negative, null,  or  insignificant  findings–frames them as something bad.

There are several reasons that negative findings are not published as often as positive findings. In the research world, there is a negative value or stigma  attached to null findings, and there is a positive value attached to significant findings. We place a cultural value on getting positive results and correctly proving your original hypothesis. The very naming of the terms themselves– negative, null, or  insignificant  findings–frames them as something bad.

The resulting stigma attached to null findings makes them a low priority for publication. Many reputable journals are  less likely to accept and publish negative findings  because they generally have a lower citation rate, and thus a  lower impact factor , and are often controversial. Also due to this stigma, many scientists who get negative results deem their work to be useless and a waste of time. As a result, they may choose not to publish them.[1] Many scientists– particularly young ones –even fear publishing negative results could negatively impact their career, even forcing them out of research altogether. This fear is not unfounded. Researchers who spend a lot of time and money on the ‘wrong’ project will likely find that their research is published with a low impact factor. As a result they could receive less funding for future research.  One site  even warns (in bold), “Do not publish negative results as a young scientist. Leave it to the senior scientists who already have a successful career and can afford it to publish negative findings for the sake of good science!” Even well-established scientists experience this pressure to publish only their significant findings. Because scientists are involved in research as a career, they naturally find themselves having to compete for positions and funding for their research.[2] Publishing negative findings could frame a scientist as ‘unsuccessful’ and make it harder for him or her to secure funding for future projects.

References: [1] Miller-Halegoua, Suzanne M. (2017). Why null results do not mean no results: negative findings have implications for policy, practice, and research. Translational Behavioral Medicine , 7(2): 137.

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Negative findings but positive contributions in cardiovascular research

Affiliations.

• 1 Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, PR China; Chongqing Institute of Cardiology, Chongqing, PR China.
• 2 Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, PR China; Chongqing Institute of Cardiology, Chongqing, PR China. Electronic address: [email protected].
• PMID: 36931498
• DOI: 10.1016/j.lfs.2023.121494

Researchers have always concluded that results that do not support the hypothesis as unimportant, unworthy, or simply not good enough for publication. However, negative findings are essential for the progress of science and its self-correcting nature. We also believe in the importance and indispensability of negative results. Therefore, in this review, we discussed the factors contributing to the publication bias of negative results and the problems to assess the factuality and validity of negative results. Moreover, we emphasized the importance of reporting negative results in cardiovascular research, including treatments, and suggest that the negative results could clarify previously controversial topics in the treatment of cardiovascular diseases and prompt the translation of research on precision cardiovascular disease prevention and treatment.

Keywords: Cardiovascular; Clinical researches; Negative results; Translational researches.

Copyright © 2023. Published by Elsevier Inc.

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Conflict of interest statement

Declaration of competing interest No conflicts of interest, financial or otherwise, are declared by the authors.

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How to respond to negative, unexpected data and results

By charlesworth author services.

• Charlesworth Author Services
• 14 December, 2020

It’s every researcher’s worst nightmare: Your data isn’t yielding the results you had expected, or your data is showing ‘negative’ results that completely negates your research aims and contradicts your hypotheses.

You might feel like your entire research project is falling apart and that you cannot move forward. However, rest assured that there are always ways to deal with unexpected data that will not only salvage your research, but also make important contributions to your field.

Results are neither ‘negative’ nor ‘positive’

First of all, try not to think of data and results as being either ‘negative’ or positive’. In research, results can always still be useful in some way by telling you something important or interesting about either your data set, methods or methodology.

If a ‘negative’ result means that your data disproves your hypothesis or does not answer your research questions in the way you expected, this does not necessarily mean that the results must be discarded or rendered useless. It is still possible to write up and publish this research, and to extract important information from the results you have obtained.

After all, ‘negative’ or unexpected results are still results – trace your research backwards and try to examine what it is that caused this result. You might find something very interesting and insightful in the methods you used. Or you might discover that the results tell you something novel, even groundbreaking, about that particular data set or the issue you are investigating.

Being able to clearly demonstrate and explain how and why a method does not work, or why a particular method produces undesirable outcomes, is itself a valuable contribution to the field. For example, in research around vaccines or medical treatments, having something not work out is not considered failure. Instead, it can help researchers eliminate what is not effective, narrow down the scope of investigation, and allow them to rule out certain methods so they can proceed to work with others.

Remember that throughout the history of scientific research, unexpected anomalies in results have often brought up surprising new discoveries or prompted scientists to investigate other novel issues. In fact, sometimes the discoveries are the anomalies or accidental ‘mistakes’ from another research project.

Talk it out

It is not uncommon for PhD students to panic when they get unexpected results. They might then try to start their project from scratch or give up altogether. However, before you resort to any extreme measures, it is really important that you speak to your supervisor and/or colleagues from your research project or department.

Your supervisor should be able to offer you more focused advice about what you can do to effectively address the specific issues arising with your data. You can talk to them about what you did during your data collection (in either labwork or fieldwork) and they can help you untangle where things may have gone wrong, how to recollect more data if necessary (and if you have time), and what else you can do at this stage to move forward with the results that you have.

Getting different perspectives and troubleshooting your process with others might also reveal that the issues you are facing with your data are not as disastrous as you think. When you talk to others, they can give you new ideas for how you can work with the data you currently have or offer suggestions for what else you can do in your situation.

It is important not to try to solve everything on your own. Remember that you have the support of your supervisors and the research community in your department and university. Don’t fear being judged for having problems with your data. All researchers understand that it is common for difficulties and issues to arise with research and data, and they are more than likely to have good advice and reassurance to help you.

Prove your mettle

Although it may not seem like it at the time, having to deal with difficult, unexpected results is an excellent opportunity for you to prove your strengths and resourcefulness as a researcher.

As you address diverse and unexpected issues arising in your research, you demonstrate your knowledge of the field and discipline. You show that you understand a range of existing theory, methodology and analytical tools, and showcase your ability to employ and extract from previous work to manage your own research – whatever the challenges. By doing this, you show how resourceful, adaptable and versatile you are as a researcher.

You would also be exercising clear researcher reflexivity, by presenting conscientious awareness of how your decisions and actions affect your data, your analysis and the overall directions of your research. You will be able to show that you have a thorough understanding of what you have done, how you have done it, and what you could have done differently.

These are all excellent and highly desirable traits of an effective researcher, and being able to exercise and prove these qualities is often more important than the findings themselves. Remember that you are being assessed not just on the research you produce, but also in your abilities to do rigorous, thoughtful research. So, take the focus off the ‘negative’ results at hand got and consider how you will effectively respond to and adapt your research instead.

Charlesworth Author Services, a Trusted Brand supporting world’s leading academic publishers, institutions and authors since 1928. We only work with native English-speaking editors with advanced or postdoctoral degrees in their disciplines. Our academic writing and publishing training courses, online materials, and blog articles contain numerous tips and tricks to help you navigate academic writing and publishing, and maximise your potential as a researcher.

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• USC Libraries
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Organizing Your Social Sciences Research Paper

• 7. The Results
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE:   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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Patient mistreatment, emotional exhaustion and work-family conflict among nurses: a moderated mediation model of social sharing of negative work events and perceived organizational support

• Wei Yan 1 ,
• Zeqing Cheng 1 ,
• Di Xiao 2 ,
• Huan Wang 3 , 4 ,
• Li Li 6 &
• Caiping Song 7  

BMC Medical Education volume  24 , Article number:  1041 ( 2024 ) Cite this article

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Nursing literature suggested that patient mistreatment has significant impacts on nurses’ emotions and job burnout. Yet, further research is needed to understand the underlying mechanism and the spillover effect on nurses’ families. Leveraging the goal progress theory, this study aimed to examine the association between patient mistreatment, nurses’ emotional exhaustion, and work-family conflict, as well as the mediating role of social sharing of negative work events and the moderating role of perceived organizational support.

During the COVID-19 pandemic in China, a cross-sectional study was conducted with a sample of 1627 nurses from the Hematology Specialist Alliance of Chongqing from October to November 2022. Questionnaires were administered to measure patient mistreatment, perceived organizational support, social sharing of negative work events, emotional exhaustion, and work-family conflict. Hierarchical linear regression and conditional processes were used for statistical analyses.

Patient mistreatment was positively associated with emotional exhaustion ( β  = 0.354, p  < 0.001) and work-family conflict ( β  = 0.314, p  < 0.001). Social sharing of negative work events played a partial mediating role in the relationship between patient mistreatment and emotional exhaustion (effect = 0.067, SE = 0.013), and work-family conflict (effect = 0.077, SE = 0.014). Moderated mediation analysis found that the mediation effect was stronger when the perceived organizational support was high.

Our findings reveal the amplifying effect of social sharing of negative work events on nurses’ emotional exhaustion and work-family conflict. Perceived organizational support strengthens the positive effect of patient mistreatment on the social sharing of negative work events, thus resulting in increased emotional exhaustion and work-family conflict. We also discuss practical implications, limitations, and directions for future research.

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Introduction

With the outbreak of COVID-19, workplace violence in medical organizations have intensified, putting tremendous pressure on healthcare workers [ 1 , 2 ]. A survey of 522 Chinese nurses found that 55% of respondents had experienced workplace violence in the past 12 months, including verbal and physical aggression [ 3 ]. Workplace violence directly affected nurses’ job performance and organizational citizenship behavior [ 4 , 5 ], reduced their quality of life [ 6 ], and increased their psychological distress and turnover intention [ 7 , 8 ]. Among these, the behavior of patients and their families abusing nurses through insults, unreasonable demands, or physical attacks was described as patient mistreatment [ 9 ]. Previous research has confirmed that when nurses were mistreated by patients, they may experience persistent work meaninglessness, emotional exhaustion and depression [ 10 , 11 , 12 ], which further predicted their career withdrawal behavior and turnover intention [ 12 ].

Additionally, the negative impact of experiencing abuse from service users may spread from service providers to their families [ 13 ]. Research on customer mistreatment has shown that abusive stress events encountered by front-line service providers in the workplace can detrimentally affect their role performance in the family domain [ 14 ], consume additional resources, and lead to work-family conflict [ 15 ].

Some studies indicate that after experiencing negative events, individuals tend to share negative events with their families or friends to alleviate negative emotions [ 16 , 17 ]. However, whether social sharing of negative work events can attenuate emotional exhaustion and reduce work-family conflict remains undetermined [ 18 ]. In this study, we examine the mediating effect of social sharing of negative work events between patient mistreatment and emotional exhaustion and work-family conflict.

One common solution for employees to address workplace violence was to seek organizational support [ 19 , 20 ]. However, evidence from several studies suggested that organizational support didn’t mitigate the relationship between workplace violence and stress [ 21 , 22 , 23 ], suggesting that the benefit of organizational support is controversial. Thus, this study explores the moderating role of perceived organizational support (POS) in the effect of patient mistreatment on emotional exhaustion and work-family conflict via social sharing of negative work events.

It can be observed that existing literature on the functional mechanism of patient mistreatment and its spillover impact on nurses’ family domain remains rare and far from unanimous. Therefore, this study establishes and tests a theoretical model of the effects of patient mistreatment on nurses’ emotional exhaustion and work-family conflict and explores the underlying mechanism and boundary condition of this relationship. To be more specific, we aim to answer the following questions: Does patient mistreatment increase nurses’ emotional exhaustion and work-family conflict through social sharing of negative work events? Could perceived organizational support intensify the mediating effect of social sharing of negative work events?

Patient mistreatment, emotional exhaustion and work-family conflict

Similar to customer mistreatment, patient mistreatment occurs when nurses experience unfair interpersonal treatment from patients and their families, such as various forms of verbal attacks, including anger, cursing, shouting, and rudeness [ 24 , 25 ]. Patient mistreatment adversely affects the quality of medical service and work performance of nurses, and imposes threats to their mental health. Previous studies have shown that employees exhibit negative emotions such as emotional dysregulation, declining morale, and post-traumatic stress disorder [ 7 , 18 , 26 ] when frequently or intensely exposed to interpersonal mistreatment. Negative job attitudes predict subsequent burnout, withdrawal, and service-destroying behaviors [ 27 , 28 , 29 , 30 ]. Drawing on the goal progress theory [ 31 ], we argue that patient mistreatment interrupts the service achievement process, and the failure of achieving service goals triggers a constantly cognitive rumination process that could result in continuous emotional exhaustion [ 32 , 33 , 34 ]. Research has also shown that individuals who experience customer abuse in the workplace may transfer their negative emotions to family members [ 14 ]. This causes the harmful effects of customer mistreatment to spread throughout the area of employees’ family life [ 35 ]. Hence, it can be expected that:

Patient mistreatment is positively correlated with emotional exhaustion.

Patient mistreatment is positively correlated with work-family conflict.

Patient mistreatment and social sharing of negative work events

Empirical evidence showed that individuals tend to share negative experiences with peers and friends in search of emotional support and to reduce burnout [ 18 , 36 ], which may occur from a few hours to several months after the event. This kind of sharing of negative sentiments in a relatively trusted environment can be generalized as social sharing of negative work events [ 18 ]. The more frequently nurses experience mistreatment, the stronger their intentions of social sharing will grow. Accordingly, we propose the following hypothesis:

Patient mistreatment is positively correlated with social sharing of negative work events.

The mediating role of social sharing of negative work events

Social sharing involves confronting negative emotions and expressing them verbally in a safe environment [ 37 ]. However, social sharing of negative work events may be a maladaptive coping strategy that employees adopt when facing patient mistreatment, falling under the domain of social cognitive rumination [ 18 , 38 ]. According to the goal progress theory, social sharing further promotes repetitive discussions or rehearsals of negative events [ 39 ]. It can engulf nurses in work rumination, affecting their subsequent work engagement [ 40 ]. Employees who are deeply immersed in negative work events for a long time may find it difficult to detach themselves from work and are unable to address the emotional needs generated by rumination [ 41 ]. Jeon (2021) also found that emotional rumination caused by work communication resulted in more emotional exhaustion [ 42 ]. Huang (2022) demonstrated that when peers engage in co-rumination due to negative events, it exacerbated working pressure, negative moods and psychological problems [ 43 ]. We believe that sharing negative events within a social context leads to a more negative view of patient mistreatment, thus aggravating emotional exhaustion after work [ 44 ] and causing further depletion of nurses’ cognitive and emotional resources [ 45 ].

Additionally, when employees focus on negative work events for extended periods, they invest a significant amount of time and energy into uncompleted work goals, thereby disrupting the time that could be allocated to family activities, often leading to disappointment and frustration for both employees and their families [ 46 , 47 ]. It is documented that individuals subjected to severe customer mistreatment have fewer resources available to meet family needs, thereby increasing work-family conflict (WFC) [ 48 ]. The repetitive thinking triggered by negative work events makes it difficult for individuals to sufficiently engage in family roles, thus resulting in negative emotions spilling over from workplace into family life [ 14 , 49 , 50 , 51 , 52 ]. Park and Kim (2019) also articulated that the harmful effects of customer mistreatment extended into the personal life domain [ 35 ]. Thus, we propose the following hypotheses:

Social sharing of negative work events plays a mediating role between patient mistreatment and emotional exhaustion.

Social sharing of negative work events plays a mediating role between patient mistreatment and work-family conflict.

The moderating role of perceived organizational support

Perceived organizational support refers to the overall perception of employees regarding the organization’s willingness to help them, value their contributions, and care about their overall well-being [ 53 ]. It is commonly believed to be helpful in dealing with the problems such as work frustration and burnout [ 54 , 55 ]. POS meets the socio-emotional needs of respect, belonging, emotional support and recognition in the workplace [ 56 ], providing a safer and more trusted environment in which employees are more likely to share negative events with colleagues or peers [ 57 ]. We propose that:

H4. Perceived organizational support moderates the relationship between patient mistreatment and social sharing of negative work events, and this positive relationship is stronger when perceived organizational support is high (vs. low).

As elaborated in H3, patient mistreatment could be perceived by nurses as a failure of personal service goals, indicating that nurses have not successfully fulfilled their obligations and job requirements. This brings huge psychological and role pressure [ 58 , 59 , 60 ]. Perceived stress leads to negative emotional focus and cognitive rumination, which manifests as recursive thinking and sharing of negative work events, thus triggering job burnout [ 61 ]. Combining Hypotheses 1, 2, 3a, 3b and 4, we propose that the mediating effect of social sharing of negative work events will be moderated by perceived organizational support:

H5a . Perceived organizational support moderates the indirect influence of.

patient mistreatment on emotional exhaustion through social sharing of negative work events, and the indirect influence is stronger when the level of perceived organizational support is high (vs. low).

H5b. Perceived organizational support moderates the indirect influence of.

patient mistreatment on work-family conflict through social sharing of negative work events, and the indirect influence is stronger when the level of perceived organizational support is high (vs. low).

We summarize our conceptual model in Fig.  1 .

Conceptual model

Study design and setting

This study exploited a cross-sectional design to investigate the relationship between patient mistreatment, emotional exhaustion, and work-family conflict among Chinese nurses during the COVID-19 pandemic after the lockdown was imposed in mainland China. During the pandemic, our participants performed heavy work tasks and experienced psychological stress.

Participants and data collection

Collaborating with the Chongqing Hematology Specialist Alliance, we initiated a call for research on patient mistreatment and obtained a convenient sample. Clinical nurses were invited to participate in the survey through one-to-one contact. The inclusion criteria were as follows: (1) possession of a nursing practice license; (2) working as a clinical nurse; and (3) informed consent and voluntary participation. The exclusion criteria were as follows: (1) nurses with further education; (2) interns; (3) trainees; and (4) off-duty nurses (on leave, sick leave, or out for studying). To prevent COVID-19 risk, we used an online electronic questionnaire for ease of operation.

A small-scale pilot survey was conducted before the formal survey to ensure the rationality of questions and the accuracy of expressions. An anonymous cross-sectional online survey was conducted via the questionnaire website of Wenjuanxing (link: https://www.wjx.cn/ ) from October 9 to November 1, 2022. Finally, we obtained a sample of 1627 valid responses.

The measurement used was originally published in English; therefore, we adopted Brislin’s (1986) suggestion and translated the scale forward and backward to ensure Chinese equivalence and prevent semantic bias problems [ 62 ].

• Patient mistreatment

We measured patient mistreatment using the 18 items developed by Wang et al. (2011) [ 63 ]. Some minor modifications were made to suit the hospital environment since the original scale was designed to assess customer mistreatment. Sample items included “Patients demanded special treatment” and “Patients took their bad temper out on you”. The respondents reported the frequency with which they had experienced mistreatment from their patients within the last three months. Each item was measured on a 5-point Likert scale (“0” = never and “4” = all of the time). The alpha coefficient was 0.95.

• Social sharing of negative work events

We used the four items developed by Baranik et al. (2017) to capture the social sharing of negative work events [ 18 ]. Participants were asked how frequently they had talked about unpleasant things that had occurred at work in the past month with their lovers, family members, friends, and coworkers. Responses were recorded on a five-point scale (“0” = never and “4” = often). The Cronbach’s alpha coefficient was 0.86.

• Emotional exhaustion

Emotional exhaustion was measured using the emotional exhaustion component of Maslach et al.‘s (2001) MBI scale [ 64 ], which consisted of nine items. Sample items included “I feel emotionally drained from my work.” Responses were made on a seven-point scale (“1” = never and “7” = every day). The alpha coefficient for this scale was 0.93.

• Work-family conflict

Work-family conflict was measured using the five-item subscale of Netemeyer et al.’s (1996) [ 46 ]. A sample item is “The stress of my job makes it difficult for me to meet my family responsibilities.” Participants indicated their agreement with the items on a 7-point Likert scale (“1” = strongly disagree and “7” = strongly agree). The alpha coefficient for this scale was 0.94.

• Perceived organizational support

We used the eight items developed by Shen and Benson (2016) to measure perceived organizational support [ 65 ]. Sample items included “My organization values my contributions to the organization” and “The organization really cares about my health and welfare.” Responses were recorded on a seven-point Likert scale (“0” = strongly disagree and “6” = strongly agree). The alpha coefficient for the entire scale was 0.90.

Control variables

Following previous studies [ 19 , 20 ], we controlled for nurses’ gender, age, education, working years and position, all of which have been shown to possibly correlate with emotion exhaustion and work-family conflict. In addition, we controlled for marital status and children, two variables that may have an impact on work-family conflict [ 66 , 67 ].

Statistical analysis

We used SPSS 25.0, Amos 23.0 and Mplus 8.5 for data analysis. Descriptive statistics were used to present the demographic characteristics of the sample. Pearson correlation analysis was used to explore the correlations among patient mistreatment, social sharing of negative work events, perceived organizational support, emotional exhaustion, and work-family conflict. Harman’s single factor analysis and the confirmatory factor analysis were used to investigate the common methods variance (CMV). In addition, we tested the hypotheses using hierarchical regression analysis, bootstrapping tests, and conditional process analysis (specifically, moderated mediation in this study).

Characteristics of participants

The demographic characteristics of the participants are presented in Table  1 . A total of 1627 nurses participated in the study, with a mean age of 31.3 years (SD = 6.0). Among them, 94.7% were female and 5.3% were male. The average number of working years was 9.3 (SD = 6.4). Most participants were married (62.6%) and had undergraduate degree (89.7%). 76.8% of participants were primary nurses. More than half of the participants had children (56.5%).

Correlations among variables

Table  2 presents the means, standard deviations, and correlations of all the measured variables. First, the results indicated that patient mistreatment was positively correlated with social sharing of negative work events ( r  = 0.198, p  < 0.01), emotional exhaustion ( r  = 0.361, p  < 0.01) and work-family conflict ( r  = 0.316, p  < 0.01), and negatively correlated with perceived organizational support ( r =-0.319, p  < 0.01). Furthermore, social sharing of negative work events, emotional exhaustion, and work-family conflict were all negatively correlated with perceived organizational support ( r =-0.193, p  < 0.01; r =-0.471, p  < 0.01; r =-0.460, p  < 0.01; respectively).

We used the Harman single-factor test to assess the common method variance (CMV). Factor analysis shows that the first principal component explained 33.20% of total variance, suggesting that the same source bias is not severe in this study. Before testing our hypotheses, we conducted confirmatory factor analyses (CFA) to confirm the factor structure of our measurement model. As shown in Table  3 , the proposed five-factor model fits the data better: χ 2  = 2492.156, df  = 831, Confirmatory Fit Index (CFI) = 0.971, Tucker-Lewis Index (TLI) = 0.970, and root-mean-square error of approximation (RMSEA) = 0.035. Thus, the distinctiveness of key constructs is supported [ 68 ].

Testing for the mediating effect

We used hierarchical regression and bootstrapping technique to test the mediation hypotheses. As shown in Table  4 , patient mistreatment was positively associated with emotional exhaustion in Model 5 ( β  = 0.354, p  < 0.001) and work-family conflict in Model 8 ( β =  0.314, p <  0.001), thus supporting H1. The test for the mediating effect followed the recommended procedures by Baron and Kenny (1986) [ 69 ]. First, Model 2 indicated a positive correlation between patient mistreatment and social sharing of negative work events ( β  = 0.201, p  < 0.001), supporting H2. Second, Model 6 and Model 9 indicated that social sharing of negative work events was positively associated with both emotional exhaustion ( β  = 0.199, p  < 0.001) and work-family conflict ( β =  0.206, p  < 0.001). Finally, although patient mistreatment was still significantly associated with emotional exhaustion in Model 6 ( β  = 0.314, p  < 0.001) and work-family conflict in Model 9 ( β  = 0.272, p  < 0.001) after the introduction of mediation variables, the size of effects was slightly weakened, suggesting that there exists a partial mediation effect.

We also calculated the indirect effects of patient mistreatment on two outcome variables via social sharing of negative work events and its 95% confidence interval, which was repeated 5000 times using bootstrapping technique. Bootstrapping is useful for testing indirect effects because it produces a repeated replacement sampling distribution of indirect effects rather than assuming a normal distribution (Preacher and Hayes, 2008) [ 70 ]. The results are presented in Table  5 . Social sharing of negative work events significantly mediated the relationship between patient mistreatment and emotional exhaustion (estimate = 0.067, 95% CI = [0.043, 0.094]) and work-family conflict (estimate = 0.077, 95% CI = [0.050, 0.108]). Taken together, these results support H3a and H3b.

Testing for the moderated mediation effect

In our conceptual model, perceived organizational support was proposed to moderate the relationship between patient mistreatment, emotional exhaustion and work-family conflict via social sharing of negative work events. Following Aiken and West (1991), we mean-centered the variables used to form the interaction term [ 71 ]. As shown in the Model 3 of Table  4 , the interaction between patient mistreatment and perceived organizational support was significantly correlated with social sharing of negative work events ( β  = 0.074, p  < 0.01), supporting H4.

We used the Process plug-in to conduct a simple slope analysis [ 70 , 72 ]; the results are shown in Table  6 . The interaction patterns are shown in Fig.  2 . The graph shows that when perceived organizational support was low (-1SD), patient mistreatment was positively correlated with social sharing of negative work events (simple slope = 0.156, p  < 0.001), which was smaller than the coefficient when perceived organizational support was high (+ 1 SD) (simple slope = 0.338, p  < 0.001).

Moderating effect of POS on the relationship between patient mistreatment and social sharing of negative work events. Note PM = Patient Mistreatment; POS = Perceived Organizational Support; SS = Social Sharing of Negative Work Events

Finally, we used Mplus 8.5 to examine the moderated mediating effects. The results in Table  7 show that the indirect effect of patient mistreatment on emotional exhaustion via social sharing of negative work events was positive and statistically significant when perceived organizational support was low (estimate = 0.029, 95% CI = [0.013, 0.047]) and high (estimate = 0.060, 95% CI = [0.035, 0.092]) There was a significant difference in indirect effects between high and low perceived organizational support (estimate = 0.037, 95% CI= [0.005, 0.074]), supporting H5a. Similarly, the indirect effect of patient mistreatment on work-family conflict via social sharing of negative work events was significant when perceived organizational support was low (estimate = 0.033,95% CI = [0.015, 0.055]) and high (estimate = 0.070, 95%CI = [0.039, 0.106]). The difference in indirect effects between high and low perceived organizational support was significant (estimate = 0.037, 95% CI= [0.005, 0.074]), supporting H5b.

In addition, we use the Johnson-Neyman method to depict continuous confidence intervals for indirect effects [ 73 ]. Figure  3 shows that the continuous intervals of indirect effect are greater than zero, and increasing with the perceived organization support. The higher the perceived organizational support, the stronger the effect of patient mistreatment on emotional exhaustion through social sharing of negative work events. Figure  4 shows similar pattern when work-family conflict is the outcome variable.

Conditional indirect effects of patient mistreatment on emotional exhaustion (via social sharing of negative work events) at different levels of perceived organizational support (POS)

Conditional indirect effects of patient mistreatment on work-family conflict (via social sharing of negative work events) at different levels of perceived organizational support (POS)

Leveraging the goal progress theory, this study found that social sharing of negative work events mediated the relationship between patient mistreatment and work-family conflict and emotional exhaustion. The results of the moderated mediation analysis showed that the indirect effects of social sharing of negative events on the two outcomes caused by patient mistreatment were stronger among nurses with high (vs. low) perceived organizational support.

Our study contributes to the literature on the adverse consequences and negative emotions associated with patient mistreatment in several ways. Firstly, the research expands the scopes of literature on the outcomes of patient abuse by innovatively introducing the work-family conflict into the model. Previous research mainly focused on personal aspects directly related to work such as sleep quality, job satisfaction, and career withdrawal [ 27 , 74 , 75 ]. Our findings indicate that the boundary between work and family life is permeable, and negative emotions may flow from the work area into the family domain, causing certain conflicts.

Secondly, based on the goal progress theory [ 31 ], we explored the mediating role of social sharing of negative work events between patient mistreatment and negative outcomes, filling the research gap in this area. The social sharing of negative work events may be a maladaptive coping mechanism in stressful environments. It is a process of social cognitive rumination of service failure that challenges the self-concept of nurses and a typical manifestation of shared ruminative thinking that hinders the positive thinking at individual and/or team levels [ 34 ]. Our findings suggest that patient mistreatment, as a source of stress, produces a sufficiently long duration of negative emotions, which will be further amplified in the process of social sharing [ 40 ], eventually affecting the role conflict between work and family [ 43 ].

Thirdly, we incorporated perceived organizational support as a boundary condition and investigate its moderating role in the effects of patient mistreatment on emotional exhaustion and work-family conflict via social sharing of negative work events. The higher the perceived organizational support, the more likely employees were to experience severe rumination, resulting in further burnout. Perceived organizational support does not always produce positive outcomes [ 58 ] and in some circumstances it enhances the rumination of negative events, leading to greater occupational and psychological stress [ 61 ]. This finding enriches our understanding of the mechanism by which patient abuse affects nurses’ emotions and reactions in the context of the pandemic.

Limitations and future directions

This study has several limitations. First, our research was conducted in the context of the Confucian Chinese culture. Thus, Chinese nurses tend to show greater tolerance for patient mistreatment, since considering the overall interests of the organization is of great priority in a collective society. However, the same result may not hold for individualistic cultures. It is important to consider whether similar conclusions can be drawn in different cultural contexts.

Second, this cross-sectional study required nurses to recall patient mistreatment and negative emotions over previous months. Nurses’ subjective recall may have produced retrospective bias. Future research should use diary studies or experience-sampling techniques to record changes or fluctuations in patient mistreatment and nurses’ emotions over time.

Moreover, our findings supported the negative influences of the patient mistreatment. However, effective alleviations or remedies remained largely unexplored. It is highly recommended to study mindfulness interventions and other mechanisms to deal with patient mistreatment [ 74 ].

Practical implications

Previous research has indicated that patient mistreatment decreases frontline nurses’ job enthusiasm, thereby damaging job satisfaction and triggering withdrawal behaviors and dysfunction in the work-family domain [ 27 , 44 , 75 ]. This study shows that Chinese nurses suffer from emotional exhaustion and work-family conflict caused by patient mistreatment. Managers can employ certain techniques during recruitment to select individuals who are better equipped to handle patients’ incivility during frontline work [ 76 , 77 ].

Moreover, managers can provide frontline staff with training and guidance, simulate scenarios of patient mistreatment, and improve their ability to address patient incivility [ 78 ]. At the meantime, managers should be careful with the polices regarding the social sharing within the organization. Too much exposure and immersion into the rumination of negative work events may deteriorate morale and cause personal and family problems. Additionally, medical professionals should be encouraged to have a positive mindset and demonstrate empathy and compassion towards patients while providing medical services to minimize unnecessary conflicts [ 22 , 79 , 80 , 81 ].

Furthermore, hospital managers can establish eye-catching signs and indicators to guide patients to behave correctly and maintain a civilized manner throughout the treatment process. Society should collaborate with hospitals to create an appropriate medical environment for all patients by encouraging them and their families to take respectful and responsible actions, which will help nurses improve their work efficiency [ 82 ].

This study provides empirical evidence that patient mistreatment causes nurses’ emotional exhaustion and work-family conflict through the social sharing of negative work events. The findings of this study enrich the understanding of the mediating mechanism of patient mistreatment affecting nurses’ emotions and work-family conflict. We also reveal how perceived organizational support, as a moderating variable, enhances the positive relationship between patient mistreatment and the social sharing of negative work events and highlight that organizational support could result in greater psychological stress and family-related conflicts induced by patient mistreatment and mediated by social sharing of negative work events. Therefore, to effectively deal with patient mistreatment, hospital managers should provide training and other resources to nurses, help them regulate their negative emotions, and achieve a balance between work and family. Finally, patients should be educated to receive medical services in a civilized manner.

Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. We affirm that the methods used in the data analyses are suitably applied to our data within our study design and context, and the statistical findings have been implemented and interpreted correctly.

Molero Jurado MDM, Gázquez Linares JJ, Pérez-Fuentes MDC. Martos Martínez Á. Aggression and burnout in nurses during COVID-19: a cross-sectional study. Nurs Health Sci. 2023;25(1):130–40.

Article   Google Scholar  

Nam S, Wong JYH, Wang T, An B, Fong DYT. Psychological distress as a mediator between workplace violence and turnover intention with caring for patients with COVID-19. Front Psychol. 2024;14:1321957.

Huang S, Zhai J, Lu X, Liang Y, Li Q, Lilenga HS. Prevalence of workplace violence in Chinese obstetric nurses under the new situation and its correlation with violence prevention knowledge-attitude-practice and climate perception: a cross-sectional study. BMC Nurs. 2023;22(1):473.

Duan X, Ni X, Shi L, Zhang L, Ye Y, Mu H, Li Z, Liu X, Fan L, Wang Y. The impact of workplace violence on job satisfaction, job burnout, and turnover intention: the mediating role of social support. Health Qual Life Outcomes. 2019;17:93.

Ferri P, Stifani S, Accoto A, et al. Violence against nurses in the triage area: a mixed-methods study. J Emerg Nurs. 2020;46:384–97.

Choi SH, Lee H. Workplace violence against nurses in Korea and its impact on professional quality of life and turnover intention. J Nurs Manag. 2017;25:508–18.

Hong S, Nam S, Wong JYH, Kim H. Post-traumatic responses to workplace violence among nursing professionals: a collaborative and comparative study in South Korea and Hong Kong. BMC Nurs. 2023;22:354.

Chowdhury SR, Kabir H, Das DC, Chowdhury MR, Chowdhury MR, Hossain A. Workplace violence against Bangladeshi registered nurses: a survey following a year of the COVID-19 pandemic. Int Nurs Rev. 2023;70:219–28.

Bies RJ. Interactional (in)justice: The sacred and the profane. In Advances in Organizational Behavior (J. Greenberg & R. Cropanzano, eds.), 2001;89 – 11. Stanford University Press, Stanford, CA.

Qi L, Wei X, Li Y, Liu B, Xu Z. The influence of mistreatment by patients on job satisfaction and turnover intention among Chinese nurses: A Three-Wave Survey. Int J Environ Res Public Health. 2020;17:1256.

Jang SJ, Son YJ, Lee H. Prevalence, associated factors and adverse outcomes of workplace violence towards nurses in psychiatric settings: a systematic review. Int J Ment Health Nurs. 2022;31:450–68.

Yan W, Bao N, Zheng S, Wang H, Yue D, Chen L. The impacts of patient mistreatment on healthcare workers’ role behaviors: a study in Chinese Fangcang shelter hospitals. BMC Nurs. 2023;22:444.

Greenbaum RL, Quade MJ, Mawritz MB, Kim J, Crosby D. When the customer is unethical: the explanatory role of employee emotional exhaustion onto work-family conflict, relationship conflict with coworkers, and job neglect. J Appl Psychol. 2014;99:1188–203.

Chi NW, Yang J, Lin CY. Service workers’ chain reactions to daily customer mistreatment: behavioral linkages, mechanisms, and boundary conditions. J Occup Health Psychol. 2018;23:58–70.

Sarwar A, Imran MK, Hafeez H, Zaheer M, Fatima T. Does workplace ostracism negatively affect family life during a pandemic? Pers Rev. 2024;53(4):900-917.

Egloff B. You are not alone - social sharing as a necessary addition to the embracing factor. Behav Brain Sci. 2017;40:e358.

Spencer L. Social sharing of negative emotional events: whether or not sharing helps depends on the Listener’s response. The Pennsylvania State University; 2019.

Baranik LE, Wang M, Gong Y, Shi. Customer mistreatment, employee health, and job performance: cognitive rumination and social sharing as mediating mechanisms. J Manage. 2017;43:1261–82.

Google Scholar  

Yeh T, Chang Y, Hsu YH, Huang L, Yang C. Causes of nursing staff burnout: exploring the effects of emotional exhaustion, work-family conflict, and supervisor support. Jpn J Nurs Sci. 2021;18(2):e12392.

Cakal H, Keshavarzi S, Ruhani A, Dakhil-Abbasi G. Workplace violence and turnover intentions among nurses: the moderating roles of invulnerability and organisational support - A cross-sectional study. J Clin Nurs. 2021;00:1–11.

Kim J. The moderating effect of social support on the relationship between violence experiences and violence responses of psychiatric nurses. J Korean Acad Psychiatr Ment Health Nurs. 2019;28(2):144.

Yeh TF, Chang YC, Feng WH, Sclerosis M, Yang CC. Effect of workplace violence on turnover intention: the mediating roles of job control, psychological demands, and social support. Inquiry. 2020;57:46958020969313.

Shahrour G, Taha I, Ali AM, Alibrahim M. The moderating role of social support on workplace violence and stress among psychiatric nurses. Nurs Forum. 2022;57:1281–8.

Grandey AA, Foo SC, Groth M, Goodwin RE. Free to be you and me: a climate of authenticity alleviates burnout from emotional labor. J Occup Health Psych. 2012;17:1–14.

Koopmann J, Wang M, Liu Y, Song Y. Customer mistreatment: a review of conceptualizations and a multilevel theoretical model. Mistreatment Organ. 2015;13:33–79.

Baranik LE, Zhu Y, Wang M, Zhuang W. When does witnessing patient mistreatment hurt nurses’ performance? Gauging the moderation roles of self-concern and other-orientation. J Manage Psychol. 2022;37:170–85.

Cai D, Li F, Feng T, Liu B, Qi L, Men C. Mistreatment from patients and nurses’ career withdrawal intention: does political skill matter? Asia Pac J Hum Resou. 2022;60:342–61.

Goussinsky R. The combined moderating effects of coworker support and occupational coping self-efficacy on the relationship between patients mistreatment and burnout. J Aggress Maltreat T. 2020;29:479–97.

Garcia PRJM, Restubog SLD, Lu VN, et al. Attributions of blame for customer mistreatment: implications for employees’ service performance and customers’ negative word of mouth. J Vocat Behav. 2019;110:203–13.

Kim S, Kitzmiller R, Baernholdt M, Lynn MR, Jones CB. Patient safety culture: the impact on workplace violence and health worker burnout. Workplace Health Saf. 2023;71:78–88.

Martin LL, Tesser A. Toward a motivational and structural theory of ruminative thought. In: Uleman JS, Bargh JA, editors. Unintended thought. New York: Guilford Press; 1989. pp. 306–26.

Arnold KA, Walsh MM. Customer incivility and employee well-being: testing the moderating effects of meaning, perspective taking and transformational leadership. Work Stress. 2015;29:362–78.

Jiang FB, Wang Z. Applications of cognitive appraisal theory of tress in managerial psychology research: scenes, methods, and myths. Adv Psychol Sci. 2022;30:2825–45.

Haggard DL, Robert C, Rose AJ. Co-rumination in the workplace: adjustment trade-offs for men and women who engage in excessive discussions of workplace problems. J Bus Psychol. 2011;26:27–40.

Park YA, Kim S. Customer mistreatment harms nightly sleep and next-morning recovery: job control and recovery self-efficacy as cross-level moderators. J Occup Health Psychol. 2019;24:256–69.

McCance AS, Nye CD, Wang L, Jones KS, Chiu C. Alleviating the burden of emotional labor: the role of social sharing. J Manage. 2013;39(2):392–415.

Zhan Y, Wang M, Shi J. Lagged influences of customer mistreatment on employee mood: moderating roles of maladaptive emotion regulation strategies. In: Ashkanasy, N.M., Zerbe, W.J., Härtel, C.E.J, editors. Research On Emotion in Organizations. Emerald Group, Bingley, UK. 2013;9:203–224. Further Reading.

Brown SP, Westbrook RA, Challagalla G. Good cope, bad cope: adaptive and maladaptive coping strategies following a critical negative work event. J Appl Psychol. 2005;90:792–8.

Nolen-Hoeksema S. Emotion regulation and psychopathology: the role of gender. Annu Rev Clin Psycho. 2012;8:161–87.

Wang HJ, Li PK, Bauer TN, Erdogan B. Patient mistreatment and new nurse adjustment: the role of rumination and work engagement. Hum Relat. 2023;0.

Geisler M, Buratti S, Allwood CM. The complex interplay between emotion regulation and work rumination on exhaustion. Front Psychol. 2019;10.

Jeon YJJ. The effects of after-working hours work contact through information and communication technology on emotional rumination, work-family conflict, and emotional exhaustion: a study of hotel and duty-free employees. J Tou Leis Res. 2021;33:407–23.

Huang YY, Bullock A, Liu J, Wang ZX, Xu G, Sang B. Co-rumination with friends exacerbates association between peer victimization and adjustment in adolescence. J Appl Dev Psychol. 2022;80:101410.

Shi X, Wang X. Daily spillover from home to work: the role of workplace mindfulness and daily customer mistreatment. Int J Contemp Hosp M. 2022;34:3008–28.

Knipfer K, Kump B. Collective rumination: when ‘problem talk’ impairs organizational resilience. Appl Psychol-Int Rev. 2022;71:154–73.

Netemeyer RG, Boles JS. Development and validation of work-family conflict and family-work conflict scales. J Appl Psychol. 1996;81:400–10.

Huyghebaert-Zouaghi T, Morin AJS, Fernet C, Austin S, Gillet N. Longitudinal profiles of work-family interface: their individual and organizational predictors, personal and work outcomes, and implications for onsite and remote workers. J Vocat Behav. 2022;134:103695.

He Y, Walker JM, Payne SC, Miner KN. Explaining the negative impact of workplace incivility on work and non-work outcomes: the roles of negative rumination and organizational support. Stress Health. 2021;37:297–309.

Junker NM, Baumeister RF, Straub K, Greenhaus JH. When forgetting what happened at work matters: the role of affective rumination, problem-solving pondering, and self-control in work-family conflict and enrichment. J Appl Psychol. 2021;106:1750–66.

Gillet N, Austin S, Huyghebaert-Zouaghi T, Fernet C, Morin AJS. Colleagues’ norms regarding work-related messages: their differential effects among remote and onsite workers. Pers Rev. 2022;53:173–92.

Zhang R, W YQ, Ferreira-Meyers K. The work-family spillover effects of customer mistreatment for service employees: the moderating roles of psychological detachment and leader-member exchange. Front Psychol 2019;10.

Karakurt N, Erden Y, Çelik AS. The relationship between nurses’ work stress levels and work-family conflict during the COVID-19 pandemic and the affecting factors: a study from Turkey. Arch Psychiat Nurs. 2023;42:61–7.

Eisenberger R, Wen X, Zheng D, Yu J, Liu Z, Zhang J, Wang L, Kim TY, Krivacek S, Zagenczyk TJ, Joo MK, Mesdaghinia S, Lee DR, Kim TH. Does felt obligation or gratitude better explain the relationship between perceived organizational support and outcomes? Group Organ Manage. 2023;0.

Yanbei R, Dongdong M, Yun L, et al. Does perceived organization support moderates the relationships between work frustration and burnout among intensive care unit nurses? A cross-sectional survey. BMC Nurs. 2023;22:22.

Bobbio A, Manganelli AM. Antecedents of hospital nurses’ intention to leave the organization: a cross sectional survey. Int J Nurs Stud. 2015;52(7):1180–92.

Baran B, Rhoades L, Miller L. Advancing perceived organizational support theory into the twenty-first century world of work. J Bus Psychol. 2012;27:123–47.

Hngoi CL, Abdullah NA, Sulaiman WSW, Nor NIZ. Examining job involvement and perceived organizational support toward organizational commitment: job insecurity as mediator. Front Psychol. 2024;15.

Pecino V, Mañas MA, Díaz-Fúnez PA, Aguilar-Parra JM, Padilla-Góngora D, López-Liria R. Organisational climate, role stress, and public employees’ job satisfaction. Int J Environ Res Public Health. 2019;16:1792.

Keller EG, Hittle BM, Boch S, Davis K, Gillespie GL. Modeling wellbeing for U.S. correctional nurses: a cross sectional survey. Int J Nurs Stud. 2023;147:104589.

Zhang J, Liu F, Smith AP. Exploring the relationship between work stress and work-related rumination. Psychol Rep. 2023;0(0).

Weigelt O, Gierer P, Syrek CJ. My mind is working overtime-towards an integrative perspective of psychological detachment, work-related rumination, and work reflection. Int J Environ Res Public Health. 2019;16:2987.

Brislin RW. The wording and translation of research instruments. In: Lonner WL, Berry JW, editors. Cross-cultural research and Methodology Series. Thousand Oaks, CA: Sage; 1986. pp. 137–64.

Wang M, Liao H, Zhan Y, Shi J. Daily customer mistreatment and employee sabotage against customers: examining emotion and resource perspectives. Acad Manage J. 2011;54:312–34.

Maslach CA, Schaufeli WB, Leiter MP. Job burnout. Annu Rev Psychol. 2001;52:397–422.

Shen J, Benson J. When CSR is a social norm: how socially responsible human resource management affects employee work behavior. J Manage. 2016;42:1723–46.

Nixon AE, Yang LQ, Zhang XC. Emotional labor in China: do perceived organizational support and gender moderate the process? Stress Health. 2011;27(4):289–305.

Labrague LJ, Ballad CA, Fronda DC. Predictors and outcomes of work-family conflict among nurses. Int Nurs Rev. 2021;68(3):349–57.

Feng TW, Huang YF, Avgerinos E. When marketing and manufacturing departments integrate: the influences of market newness and competitive intensity. Ind Market Manag. 2018;75:218–31.

Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82.

Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40:879–91.

Aiken LS, West SG. Multiple regression: testing and interpreting interactions. Thousand Oaks, CA, US: Sage; 1991.

Edwards JR, Lambert LS. Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis. Psychol Methods. 2007;12:1–22.

Hayes AF. An index and test of linear moderated mediation. Multivar Behav Res. 2015;50:1–22.

Yun M, Beehr T. When experiencing nice interactions at work: good sleep quality via well-being. Stress Health. 2024;40:e3390.

Karaeminogullari A, Erdogan B, Bauer TN. Biting the hand that heals: mistreatment by patients and the well-being of healthcare workers. Pers Rev. 2018;47:572–91.

Carlson D, Ferguson M, Hunter E, Whitten D. Abusive supervision and work-family conflict: the path through emotional labor and burnout. Leadersh Quart. 2012;23:849–59.

Giusti EM, Pedroli E, D’Aniello GE, Badiale CS, Pietrabissa G, Manna C, Badiale MS, Riva G, Castelnuovo G, Molinari E. The psychological impact of the COVID-19 outbreak on health professionals: a cross-sectional study. Front Psychol. 2020;11:1684.

Li S, Yan H, Qiao S, Chang X. Prevalence, influencing factors and adverse consequences of workplace violence against nurses in China: a cross-sectional study. J Nurs Manag. 2022;30:1801–10.

Hannemann J, Abdalrahman A, Erim Y, Morawa E, Jerg-Bretzke L, Beschoner P, Geiser F, Hiebel N, Weidner K, Steudte-Schmiedgen S, Albus C. The impact of the COVID-19 pandemic on the mental health of medical staff considering the interplay of pandemic burden and psychosocial resources-A rapid systematic review. PLoS ONE. 2022;17:e0264290.

Wieckowski AG. The emotional toll of frontline labor. Harv Bus Rev. 2023;2:48–50.

Mahoney DA, Gopisetty D, Osterberg L, Nudelman MJR, Smith-Coggins R. Patient mistreatment of health care professionals. BMC Med Educ. 2022;22:133.

van Jaarsveld DD, Walker DD, Kim S. Encouraging good behavior from your customers. Harv Bus Rev. 2023;2:56–8.

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Acknowledgements

We would like to thank all nurse participants and Zhang Yong, Li Hua, Ma Li, and Wee Chow Hou for their helpful comments as well as the seminar participants at Chongqing University, Peking University, and Nanyang Technological University.

This study was supported by the National Social Science Foundation of China (Grant number: 19BJY052, 22BGL141), National Natural Science Foundation of China (Grant number: 72110107002, 71974021), Natural Science Foundation of Chongqing (Grant number: cstc2021jcyj-msxmX0689), Fundamental Research Funds for the Central Universities (Grant number: 2022CDJSKJC14), and Chongqing Social Science Planning Project (Grant number: 2018PY76).

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Wei Yan and Zeqing Cheng designed the study and prepared the first draft of this manuscript. Di Xiao and Xin Du participated in the data analysis. Huan Wang contributed to writing and revising the manuscript. Li Li and Caiping Song contributed to data collection and analysis. All the authors have read and approved the final version of the manuscript.

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Ethical approval was obtained from the Ethics Committee of the School of Economics and Business Administration of Chongqing University (IRB No. SEBA201906). Authors explained research objectives and procedures to all participants who were assured that their participation in this study was voluntary and anonymous. All procedures performed in this study were in accordance with the ethical standards of the National Research Council and Helsinki Declaration of 2013. Informed consent was obtained from all subjects and/or their legal guardian(s).

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Yan, W., Cheng, Z., Xiao, D. et al. Patient mistreatment, emotional exhaustion and work-family conflict among nurses: a moderated mediation model of social sharing of negative work events and perceived organizational support. BMC Med Educ 24 , 1041 (2024). https://doi.org/10.1186/s12909-024-06022-9

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Impulsivity mediates the relationship between sleep quality and interpersonal functioning: a cross-sectional study in a sample of university students

• Brian J. Farrell III 1   na1 ,
• Robert W. Emmerton 1   na1 ,
• Christina Camilleri 1 &
• Stephen Sammut 1  

Sleep Science and Practice volume  8 , Article number:  16 ( 2024 ) Cite this article

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Amid the current mental health pandemic, research continues to investigate potential contributors associated with increasing levels of negative mental health. Among such contributors is sleep, which is vital for physiological and psychological functioning with potential downstream behavioral consequences, including in relation to impulsivity and social functioning. Given the significant rates of disordered sleep behaviors reported in the literature, our study sought to investigate the relationship between sleep quality, impulsivity and interpersonal functioning among university students.

An anonymous online survey was administered to university students (Ages 18 + ; N  = 526; 33% male, 67% female) addressing demographics, sleep quality, impulsivity, and interpersonal functioning.

Our findings indicate a substantial proportion of students reporting poor sleep quality and impulsivity. Moreover, higher levels of impulsivity and lower interpersonal functioning were associated with poor sleep quality. Mediation analysis revealed a significant mediating role of attentional impulsivity in the relationship between sleep quality and interpersonal functioning.

Conclusions

Repeated reports of significant levels of impulsivity underlying numerous psychiatric disorders, its prevalence socially, and the fundamental issue that impulsivity reflects (i.e., lack of self-control/self-discipline/self-regulation), suggests a necessity to reorient therapeutic efforts towards the root of the problem. Thus, efforts should seek to maximize preventative behaviors that strengthen the individual (e.g., improving sleep-related behaviors), and contribute to the building of character/virtue, through self-discipline, perseverance and consistency, in order to reduce negative outcomes (e.g., impulsivity, dysfunctional interpersonal functioning).

Introduction

Research continues to indicate growing levels of negative mental health among university students (e.g., Beiter et al., 2015 ; Bruffaerts et al., 2018 ; Emmerton et al., 2024 ; Limone and Toto, 2022 ; Lipson et al., 2019 ; Torales et al., 2019 ; Twenge et al., 2010 ). The correlates to negative mental health are many, and scientific research continues to seek further understanding of such correlates in all contexts, including in the university student population, in order to provide potential solutions. Among the numerous correlates identified in the scientific literature, including in our previous work, sleep continues to be an important source of stress/concern (Almojali et al., 2017 ; Alotaibi et al., 2020 ; Beiter et al., 2015 ; Dietch et al., 2016 ). Furthermore, substantial proportions of college students in the United States and globally report poor sleep quality and/or an inadequate amount of sleep (Albqoor and Shaheen, 2021 ; Becker et al., 2018 ; Dinis and Braganca, 2018 ; Lund et al., 2010 ; Mbous et al., 2022 ; Ramon-Arbues et al., 2022 ).

Proper sleep quality and appropriate/healthy levels of sleep play a significant restorative role physiologically, immunologically, hormonally, neurologically, and psychologically (Asif et al., 2017 ; Benington and Heller, 1995 ; Dattilo et al., 2011 ; Kim et al., 2015 ; Scott et al., 2021 ). Thus, the consequences of disturbed or inadequate sleep can have broad pathological implications ranging from the cellular to the behavioral levels. Under such situations, at the neurobehavioral level, executive functioning, which regulates basic behaviors such as attention (Logue and Gould, 2014 ) and impulse control (Hayashi et al., 2017 ; Hayashi and Washio, 2020 ; Jones et al., 2021 ; Narvaez et al., 2012 ; Reynolds et al., 2019 ), is reduced.

Effective/appropriate executive functioning is necessary in the processes of error detection (Simoes-Franklin et al., 2010 ; van Gaal and Lamme, 2012 ), emotional regulation (Mohammed et al., 2022 ) and social cue processing (Dias et al., 2022 ). Therefore, when negatively impacted by factors such as sleep deprivation, error detection becomes dysregulated (Thomas et al., 2000 ; Tsai et al., 2005 ). Additionally, emotional dysregulation can result, potentially due to a dysfunctional metabolism impacting amygdalar regulation (Benington and Heller, 1995 ; Thomas et al., 2000 ; Wu et al., 2006 ; Yoo et al., 2007 ). Consequently, this can lead to an enhanced sensitivity to negative stimuli, decreased responsiveness to long-term positive events, reduced positive moods, amplified emotional sensitivity, and increased irritability (Dinges et al., 1997 ; Sanz-Arigita et al., 2021 ; Yoo et al., 2007 ; Young and Nusslock, 2016 ; Zohar et al., 2005 ). The potential for more serious negative behaviors, including but not limited to, suicidal thoughts and behaviors, is also present (e.g., Pigeon et al., 2012 ; Tsai et al., 2005 ; Vedaa et al., 2019 ; Vyazovskiy and Delogu, 2014 ; Wu et al., 2006 ). Such consequences/behaviors not only impact the individual experiencing them, but also the social surroundings of the individual, as their behaviors can influence the way in which they interact with others.

Additionally, as indicated above, executive functioning is important in impulse control. A deficiency in impulse control can lead/contribute to a wide range of negative, disinhibited and often risky behaviors. Such behaviors include, but are not limited to, excessive texting, texting while driving, drug abuse and/or inappropriate alcohol use and risky sexual behaviors and any consequences resulting from such behaviors (Hayashi et al., 2017 ; Hayashi and Washio, 2020 ; Jentsch et al., 2014 ; Jones et al., 2021 ; Killgore et al., 2006 ; Narvaez et al., 2012 ; Potenza and de Wit, 2010 ; Reynolds et al., 2019 ). Related, inappropriate impulse control can also lead to adverse/dysfunctional social behaviors evident in such behaviors as inappropriate peer influence, social anxiety, aggression and antisocial personality disorder (aan Het Rot et al., 2015 ; DeBono et al., 2011 ; Dixon et al., 2017 ; Krakowski, 2003 ; Roeser et al., 2018 ; Swann et al., 2010 ). In addition to the potential effects of emotional dysregulation discussed above, the presence of inappropriate social behaviors, including dysregulation in social cue processing (Gilboa-Schechtman and Shachar-Lavie, 2013 ; Ruz and Tudela, 2011 ), can also subsequently influence interpersonal functioning (defined as intimacy and empathy; DSM-5) and the formation and maintenance of relationships (Albers, 2012 ; Chiu et al., 2021 ; Crocker et al., 2017 ). Ultimately, healthy social relationships, and therefore appropriate interpersonal functioning, have been shown to influence the overall well-being of the individual (e.g., Hawkley and Cacioppo, 2010 ; Hefner and Eisenberg, 2009 ).

Therefore, given 1) the prevalence and plethora of negative outcomes associated with disordered sleep behaviors on aspects of executive functioning (such as error detection, emotional regulation and impulsivity), 2) the various relationships reported in the literature between sleep, executive functioning, emotional regulation and interpersonal relationships, and 3) the importance of quality/healthy relationships in the midst of the mental health “pandemic”, our study sought to investigate the relationship between sleep quality and interpersonal functioning and the potential mediating role of impulsivity in such a relationship, in a sample of university students.

This survey research was conducted in compliance with Federal Law pertaining to the protection of human research subjects. Prior to administration of the survey, Franciscan University of Steubenville Institutional Review Board (IRB) approval was obtained (#2022–4). Our study consisted of a convenience sample of university/college (undergraduate and graduate) students from Franciscan University of Steubenville, OH, United States. An anonymous survey was sent via the university student email address, to all students taking classes at Franciscan University, who were at least 18 years of age. Over the course of two weeks (April 19th – May 3rd, 2022), the survey was administered through the online survey engine SurveyMonkey®. Prior to being provided access to the survey, participants were directed to a consent form, which detailed the confidentiality and the nature of the study and explained that participation in the study implied consent to analyze and publish the overall results. Participants who did not provide consent were directed to the Disqualification Page . The projected time of completion of the survey was approximately 15 min. The final page of the survey included a link to enter an optional drawing for one (1) of four (4) VISA gift cards ($25 each). The participants were informed that there was no possibility of linking the drawing information to that of the survey and that their information would remain confidential.

Exclusion criteria

Any individual who (1) was younger than 18 years of age ( n = 0), (2) was not a student at Franciscan University of Steubenville ( n = 4), (3) was an online-only student ( n = 185), or (4) responded “No” to the consent question ( n  = 3) was immediately directed to the Disqualification Page . Additionally, 75 individuals exited the survey prior to completing any or all of the above required criteria.

Exclusion criteria included any individual who: (1) did not complete the survey question regarding their age ( n = 14), or (2) did not provide a response to ( n  = 46) or complete the required questions ( n  = 21) for the Pittsburgh Sleep Quality Index (PSQI). The final number of participants whose responses met inclusion criteria was 526 (89% of the 593 respondents who started the survey; 22% of the 2386 students in the target population who received the invitation email), which is representative of the target population.

Survey structure

Demographic questions.

Demographic questions included: age, sex, primary major, and living status during the school year. Participants were also asked questions regarding the average number of hours a day they spend on academics outside of scheduled classes, number of credit hours currently being taken and the number of semesters they have spent living on campus at Franciscan University (including the semester currently underway), as well as on other university campuses (i.e., not Franciscan University).

Additionally, participants were asked to complete the “Daily Spiritual Experiences” domain of the Brief Multidimensional Measurement of Religiousness/Spirituality, which is comprised of a number of domains that address various aspects pertaining to religiosity and spirituality, which can be utilized separately (Fetzer Institute / National Institute on Aging Working Group, 2003 ). The “Daily Spiritual Experiences” domain consisted of 7 items measured on a six-point Likert scale (1 = Many times a day to 6 =  Never or almost never ).

Pittsburgh sleep quality index (PSQI)

The Pittsburgh Sleep Quality Index (PSQI) (Buysse et al., 1989 ) was used to assess participants’ sleep quality and disturbances over the past month. Subjects were asked questions regarding various aspects of their sleep habits, including the rating of statements on various Likert scales (e.g., 0 = Not during the past month to 3 =  Three or more times a week ). The PSQI is comprised of seven components (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, daytime dysfunction), which are “standardized versions of areas routinely assessed in clinical interviews of patients with sleep/wake complaints” (Buysse et al., 1989 ). The scores of the seven components are then summed to create a global/total PSQI score (ranging from 0–21), with higher scores being indicative of worse sleep quality. Moreover, a global PSQI score greater than 5 has been previously indicated as a reliable cutoff for indicating “poor” versus “good” sleepers (Buysse et al., 1989 ). Thus, going forward, in this study, in regards to our findings, sleep quality refers to the broad definition reflected in the PSQI consisting of the seven components addressed above, addressing both aspects of sleep quality and sleep disturbance.

The question regarding a roommate or bed partner originally included additional components scored on a four-point Likert scale. However, for the purpose of our survey, this question, which does not contribute to the global score, was modified to simply ask if the participant had a roommate and if so, whether they were in the same room or another room.

Barratt impulsiveness scale (BIS-11)

Our survey also included the Barratt Impulsiveness Scale Version 11 (BIS-11) (Patton et al., 1995 ) to assess various components of trait impulsivity. Participants were asked to indicate the appropriate response (on a four-point Likert scale: Rarely/Never to Almost always/Always ) regarding various statements pertaining to ways in which they act and think. A total score (Total BIS) was calculated, as well as scores for each of the three subscales: Attentional Impulsiveness (AttImp), Motor Impulsiveness (MotImp), Non-planning Impulsiveness (NpImp), with higher scores reflecting higher impulsivity for both the subscales and overall scores. Cronbach’s alpha indicated good internal consistency for Total BIS (α = 0.84), and acceptable for the AttImp and NpImp (α = 0.75 and 0.72, respectively) subscales and questionable for the MotImp (α = 0.64) subscale.

Functional idiographic assessment template—questionnaire—short form (FIAT-Q-SF)

The short form of the Functional Idiographic Assessment Template—Questionnaire (FIAT-Q-SF) (Darrow et al., 2014 ) was utilized to assess various aspects associated with the participants’ interpersonal relationships/functioning. Individuals were asked to indicate on a six-point Likert scale ( Strongly disagree to Strongly agree ) whether or not the statement applies to them. Based on Darrow and colleagues ( 2014 ), total overall scores (Total FIAT) were calculated, as well as total scores for each of the following factors: Avoidance of Interpersonal Intimacy (AvdInt), Argumentativeness or Disagreement (ArgDis), Connection and Reciprocity (ConRec), Conflict Aversion (ConAve), Emotional Experience and Expression (EmoExp), and Excessive Expressivity (ExcExp). Higher FIAT-Q-SF subscale or overall scores are indicative of worse interpersonal functioning.

Cronbach’s alpha pertaining to the FIAT-Q-SF indicated good internal consistency for the Total FIAT (α = 0.85), as well as the AvdInt, ArgDis, ConAve and ExcExp subscales (α = 0.88, 0.82, 0.81, 0.82, respectively). Additionally, Cronbach’s alpha indicated acceptable internal consistency for the ConRec subscale (α = 0.72) and questionable internal consistency for the EmoExp subscale (α = 0.64).

Statistical analysis

Analyses were conducted on all data remaining following the application of the exclusion criteria ( n = 526) using R version 4.3.0, SigmaPlot version 14.0 (Systat Software, Inc.) and Jamovi version 2.3.15. Proportions tests were utilized to assess (1) the percentage of individuals scoring within  poor versus good  sleep quality, (2) differences in the percentage of males and females scoring within  poor  sleep quality and (3) differences in the proportion of those reporting poor sleep quality within each of the BIS-11 scoring categories. Additionally, independent measures t-test (two-tailed) were used to assess (1) sex differences in total/global PSQI, BIS-11 and FIAT-Q-SF scores, (2) sex differences in the subscales/components of each scale utilized in this study (i.e., PSQI, BIS-11, FIAT-Q-SF), and (3) differences in BIS-11 and FIAT-Q-SF total scores, as well as the individual subscales, between participants reporting  good vs poor  sleep quality (as measured by the PSQI). Pearson correlations were utilized to assess the relationships between the PSQI, BIS-11 and FIAT-Q-SF. Given (1) the various correlations between the variables measured in this study, in addition to (2) previous literature (addressed above) indicating a relationship between sleep and factors associated with both impulsiveness and social relationships, as well as (3) previous research addressing impulsivity as a mediator between variables associated with sociality and well-being (Reichl et al., 2023 ), mediation analysis using Baron and Kenny’s criteria (Baron and Kenny, 1986 ; Shrout and Bolger, 2002 ), as well as 1000 bootstrapping replicates (Shrout and Bolger, 2002 ), was utilized to investigate the potential relationship between sleep quality (predictor) and interpersonal functioning (outcome), and the potential mediating effect of impulsiveness (mediator) on such a relationship. Differences were considered significant at  p  < 0.05.

Demographics

The distribution of participants in this survey was 33% male and 67% female, which is relatively representative of the student body at Franciscan University. The data for the demographic questions outlined above are shown across sex in Supplementary Table 1 (See Additional File 1).

Sleep quality (PSQI)

In relation to sleep quality, as measured by the PSQI, average global PSQI scores and average scores for each of the components (as outlined by Buysse et al. ( 1989 )) are shown in Table  1 . In this regard, analysis indicated significantly higher global PSQI scores in females relative to males ( p  < 0.05). Additionally, in relation to the PSQI subscales, females scored significantly higher in both Sleep disturbance ( p  < 0.05) and Daytime dysfunction due to sleepiness ( p  < 0.01) relative to males. All other comparisons were not significant (all p  > 0.05).

Analysis of sleep quality (PSQI), as defined by Buysse et al. ( 1989 ), indicated that a significantly higher proportion [χ 2 (1, N  = 454) = 97.80, p  < 0.001] of participants reported poor (66.5%) relative to good (33.5%) sleep quality. Figure  1 portrays the distribution of participants across global PSQI scores. Additionally, a significantly higher percentage [χ 2 (1, N  = 454) = 4.78, p  < 0.05] of females (70.0%) relative to males (59.2%) reported scores indicating poor sleep quality.

Distribution (percentage) of participants across sleep quality (Global PSQI scores). Coloring indicates poor (red) versus good (gray) sleep quality as defined by Buysse et al. ( 1989 ), i.e., scores greater than 5 indicate poor sleep quality

Impulsivity (BIS-11)

Average total BIS-11 scores and the average sum for each subscale ( Attentional Impulsiveness , Motor Impulsiveness and Non-planning Impulsiveness ) and standard errors of the mean are reported for the overall sample (i.e., sexes combined) and across the sexes in Table  1 , as well as statistics (i.e., t-statistics and r-squared values) for the appropriate tests. Average scores are also shown for Total BIS and each subscale across poor and good sleep quality (as measured by the PSQI), as well as appropriate statistics in Table  2 . Based on the total score cut-offs addressed in Stanford and colleagues ( 2009 ), 26.2% of respondents would be categorized as “highly impulsive” (72 or higher), 62.8% as “within normal limits of impulsiveness” (52-71) and 10.9% as “an individual that is either extremely over-controlled or who has not honestly completed the questionnaire” (< 52).

BIS-11 across sex

Analysis of the BIS-11 across the sexes (Table  1 ; Fig.  2 A) indicated no significant difference ( p  > 0.05) between males and females in the average total BIS-11 scores. Analysis also revealed significantly higher ( p  < 0.01) average Attentional Impulsiveness scores among females relative to males, while males scored significantly higher ( p  < 0.05) in Motor Impulsiveness relative to females. In relation to Non-planning Impulsiveness , there was no significant sex difference in average scores ( p  > 0.05).

Average impulsivity and interpersonal functioning subscale total scores across sex and sleep quality category. A BIS-11 (impulsiveness) subscale totals across sex; B BIS-11 subscale totals across PSQI category ( poor vs good sleep quality); C FIAT-Q-SF (interpersonal functioning) subscale totals across sex; D FIAT-Q-SF subscale totals across PSQI category. Data shown as mean ± standard error of the mean. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

BIS-11 and sleep quality

In relation to sleep quality and impulsivity, as measured by the PSQI and BIS-11, respectively, of the individuals scoring within the category of “highly impulsive”, 70.5% reported poor sleep quality. Additionally, of those scoring “within normal limits of impulsiveness”, 55.7% reported poor sleep quality, while 43.6% of those scoring in the lowest category of impulsiveness (< 52) reported poor sleep quality. Proportions test indicated a significantly higher percentage [χ 2 (2, N  = 503) = 13.76, p  < 0.01] of poor sleep quality among those reporting high impulsivity (score 72 or higher) relative to both other groups (score between 52–71: p  < 0.05; score < 52: p  < 0.01).

Additionally, in relation to impulsivity across sleep quality (Table  2 ; Fig.  2 B), analysis indicated that those reporting poor sleep quality indicated significantly higher average BIS-11 total scores ( p  < 0.001) than those reporting good sleep quality.

Analysis also revealed significantly higher average Attentional Impulsiveness ( p  < 0.001) and Non-planning Impulsiveness ( p  < 0.05) scores, but not Motor Impulsiveness scores ( p  > 0.05), in participants reporting poor relative to good sleep quality.

Interpersonal functioning (FIAT-Q-SF)

Average total FIAT-Q-SF scores and the average sum for each subscale ( Avoidance of Interpersonal Intimacy , Argumentativeness or Disagreement , Connection and Reciprocity , Conflict Aversion , Emotional Experience and Expression , Excessive Expressivity ) and standard errors of the mean are reported for the overall sample (i.e., sexes combined) and across the sexes in Table  1 , as well as the appropriate statistics (i.e., t -statistics and r -squared values). Average scores are also shown for Total FIAT and each subscale across poor and good sleep quality (as measured by the PSQI), in addition to the appropriate statistics in Table  2 .

FIAT-Q-SF across sex

In relation to the FIAT-Q-SF across the sexes (Table  1 ; Fig.  2 C), analysis of the total FIAT-Q-SF scores indicated no significant difference between males and females ( p  > 0.05). Additional analysis indicated various sex differences across the FIAT-Q-SF subscales. Specifically, males reported significantly higher scores relative to females in Argumentativeness or Disagreement and Connection and Reciprocity (both p  < 0.01), while females reported significantly higher Conflict Aversion scores ( p  < 0.001). All other comparisons were not significant (all p  > 0.05).

FIAT-Q-SF and sleep quality

Analysis pertaining to sleep quality (PSQI) and interpersonal functioning (FIAT-Q-SF) (Table  2 ; Fig.  2 D) indicated significantly higher ( p  < 0.001) Total FIAT scores in those reporting poor relative to good sleep quality. Additionally, analysis indicated significantly higher average Avoidance of Interpersonal Intimacy , Emotional Experience and Expression and Excessive Expressivity scores (all p  < 0.01) in those reporting poor relative to good sleep quality.

Correlation of PSQI, BIS-11 and FIAT-Q-SF

Correlation analysis indicated various significant positive relationships between the variables measured pertaining to sleep quality (Global PSQI), impulsivity (BIS-11; Total BIS, Attentional Impulsiveness, Motor Impulsiveness , Non-planning Impulsiveness ), and interpersonal functioning (FIAT-Q-SF; Total FIAT, Avoidance of Interpersonal Intimacy , Argumentativeness or Disagreement , Connection and Reciprocity , Conflict Aversion , Emotional Experience and Expression , Excessive Expressivity ) (Fig.  3 ).

Correlation plot of sleep quality (PSQI), impulsivity (BIS-11) and interpersonal functioning (FIAT-Q-SF). PSQI : Global PSQI Score; BIS-11 : BIS, Total BIS; AttImp, Attentional Impulsiveness; MotImp, Motor Impulsiveness; NpImp, Non-planning Impulsiveness; FIAT-Q-SF : FIAT, Total FIAT; AvdInt, Avoidance of Interpersonal Intimacy; ArgDis, Argumentativeness or Disagreement; ConRec, Connection and Reciprocity; ConAve, Conflict Aversion; EmoExp, Emotional Experience and Expression; ExcExp, Excessive Expressivity. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Mediation analysis of PSQI, BIS-11 and FIAT-Q-SF

Mediation analysis was performed to assess the potential mediating role of impulsiveness (as measured by the BIS-11 subscales; Attentional Impulsiveness, Motor Impulsiveness , Non-planning Impulsiveness ; M = mediator in Fig.  4 ) on the relationship between sleep quality (as measured by the Global PSQI Score; PSQI/IV = independent variable in Fig.  4 ) and interpersonal functioning (as measured by the Total FIAT-Q-SF Score; FIAT/DV = dependent variable in Fig.  4 ). Figure  4 shows both the hypothesized conceptual model (Fig.  4 A) and the final model (Fig.  4 B). Significant mediation pathways, including the indirect relationships, shown in-text (full results shown in Supplementary Table 2 (See Additional File 2)), revealed that the total effect of sleep quality (PSQI) on interpersonal functioning (FIAT-Q-SF) was significant (H1: β = 1.727, t = 6.09, p  < 0.001).

The impact of sleep quality (PSQI) on interpersonal functioning (FIAT-Q-SF), mediated through impulsiveness (BIS-11). A Hypothesized mediation model; B Final mediation model. PSQI : Global PSQI Score; BIS-11 : AttImp, Attentional Impulsiveness; MotImp, Motor Impulsiveness; NpImp, Non-planning Impulsiveness; FIAT : FIAT-Q-SF Total Score. Data represents estimates/betas—direct (total). IV, independent variable; DV, dependent variable; M, mediator. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

With the inclusion of the mediating variables (BIS-11: Attentional Impulsiveness, Motor Impulsiveness , Non-planning Impulsiveness ), the impact of sleep quality (PSQI) on interpersonal functioning (FIAT-Q-SF) was still found significant (β = 0.747, t = 2.48, p  < 0.05).

The indirect effect of PSQI on FIAT-Q-SF was found significant through Attentional Impulsiveness (β = 0.987, SE = 0.180, t = 5.47, p  < 0.001, 95% CI = 0.678, 1.392). However, the indirect effect of PSQI through both Motor Impulsiveness (β = -0.116, t = -1.76, p  = 0.079) and Non-planning Impulsiveness (β = 0.110, t = 1.74, p  = 0.083) were not significant.

Our study sought to explore the relationship between sleep quality, impulsivity and interpersonal functioning, including the potential mediating role of impulsivity in the dynamic of sleep quality and interpersonal functioning. Our study confirms the significant presence of poor sleep quality and impulsivity among university students. Moreover, corroborating previous reports, our results support the relationship between disordered sleep behaviors, higher levels of impulsivity and lower interpersonal functioning (aan Het Rot et al., 2015 ; Beattie et al., 2015 ; Cheng et al., 2021 ; DeBono et al., 2011 ; Dorrian et al., 2019 ; Gillett et al., 2021 ; Gordon et al., 2021 ; Parks et al., 2021 ).

In relation to sex differences, females reported a significantly higher prevalence of poor sleep quality (Global PSQI) relative to males, supporting previous findings (Mong and Cusmano, 2016 ; Sa et al., 2019 ). Additionally, while there were no significant sex differences in  overall impulsivity (Total BIS) and interpersonal functioning (Total FIAT), differences were observed between males and females in relation to some of the subscales, specifically, Attentional Impulsiveness and Motor Impulsiveness (for the BIS-11), as well as Argumentativeness or Disagreement , Connection and Reciprocity , and Conflict Aversion (for the FIAT-Q-SF).

Pertaining to the impact of sleep quality on impulsivity, individuals reporting poor sleep quality also reported significantly higher levels of overall impulsivity, and higher scores in the specific subscales of Attentional and Non-planning Impulsiveness . Moreover, individuals with poor sleep quality also reported significantly higher total FIAT-Q-SF scores, indicating worse overall interpersonal functioning. Furthermore, within the specific subscales pertaining to interpersonal functioning, those reporting poor sleep quality also reported higher scores in Avoidance of Interpersonal Intimacy , Emotional Experience and Expression and Excessive Expressivity . Correlation analysis revealed significant relationships between sleep quality and various aspects of impulsivity and interpersonal functioning, corroborating the dynamics previously discussed. Additionally, mediation analysis indicated that Attentional Impulsiveness (AttImp), but not Motor (MotImp) or Non-planning Impulsiveness (NpImp), significantly mediated the relationship between sleep quality (PSQI) and interpersonal functioning (FIAT).

Interpersonal functioning, sleep and basic human functioning

In relation to interpersonal functioning, human interactions and relationships are fundamental/essential for normal human behavior (Hawkley and Cacioppo, 2010 ; Hefner and Eisenberg, 2009 ). Deprivation from such interactions is known to result in detrimental physiological, and ultimately, psychological effects (Leigh-Hunt et al., 2017 ; Loades et al., 2020 ; Sepulveda-Loyola et al., 2020 ; Taylor et al., 2018 ). However, the interaction between physiology and complex behaviors, such as those observed in human interactions, is not only bidirectional, but is also mediated/impacted by innate behaviors such as sleep. Through its impact on normal physiological functioning, including but not limited to, molecular and neuronal processes (e.g., Abel et al., 2013 ; Gaine et al., 2018 ; Leproult and Van Cauter, 2010 ), appropriate/inappropriate sleep behaviors (e.g., proper sleep vs insufficient sleep/sleep deprivation/sleep loss, etc.) can significantly impact basic human functioning in relation to learning, memory, decision-making, concentration/attention, and resilience/tolerance to stress (Chatburn et al., 2013 ; Chattu et al., 2018 ; Harrison and Horne, 2000 ; Hudson et al., 2020 ; Kechter and Leventhal, 2019 ; Maquet, 2001 ; Rasch and Born, 2013 ; Tempesta et al., 2018 ; Walker and Stickgold, 2005 ). Consequently, behaviors are impacted at an individual level (e.g., lead to irritability) with subsequent consequences on human relationships/interactions (e.g., Ben Simon et al., 2022 ; Chattu et al., 2018 ; Whiting et al., 2023 ). Our findings appear to support these dynamics through the relationships observed between sleep quality and both impulsivity and interpersonal functioning; specifically, lower sleep quality was associated with higher impulsivity scores and worse interpersonal functioning, thus reinforcing the importance of proper sleep in order to minimize the potential for the dysfunctional behaviors, interactions and relationships addressed above, which can, in the big picture, lead to dysfunction in various life situations including, but not limited to work, school environments, as well as intimate relationships. Additionally, our findings demonstrate that impulsivity (specifically attentional impulsivity) plays a significant role in mediating the relationship between sleep and interpersonal functioning.

While various pharmacological agents can be utilized to buffer the effects of some inappropriate sleep behaviors, such as sleep deprivation, their efficacy is often limited and temporary (e.g., Aggarwal et al., 2011 ). On the contrary, a more suitable treatment option may be adjustments in behavior such as improving sleep hygiene and routines, which are utilized in behavioral interventions, such as cognitive behavioral therapy for insomnia (CBT-I), which have been shown to be more effective in assisting individuals suffering from sleep issues such as insomnia in the long-term (Schlarb et al., 2018 ; Trauer et al., 2015 ; Wu et al., 2015 ). This further highlights the importance of focusing efforts on changing behaviors, rather than simply treating the symptoms associated with poor sleep.

Interpersonal functioning, sleep and executive function

It is important to note that interpersonal functioning is highly influenced by executive functioning  (Lewis and Carpendale, 2009 ; Madjar et al., 2019 ) through various factors that can be modulated by  sleep , such as emotional regulation, decision-making and attention, as indicated above. In fact, at a neurological level, amygdalar (emotion) and ventromedial prefrontal cortex (executive function) dysfunction (which have been shown to relate to sleep deprivation (Goldstein and Walker, 2014 ; Libedinsky et al., 2011 )) have been reported to detrimentally impact emotional control, decision-making and attention (Dorrian et al., 2019 ; Wolf et al., 2014 ). This dynamic could potentially be underlying our findings pertaining to the impact of sleep quality on interpersonal functioning, particularly those aspects potentially influenced by emotional regulation (e.g., the  Emotional Experience and Expression subscale of the FIAT-Q-SF).

Additionally, attention (which, again, is significantly impacted by executive functioning) is fundamental to all behavior and significantly informs decision-making at all stages of human life (e.g., Huttermann et al., 2018 ; Rangelov and Mattingley, 2020 ). The impact of poor sleep quality on executive functioning may also be reflected in the correlation/relationship between sleep quality and impulsivity, most especially in relation to  attentional impulsiveness  (which represents attention and cognitive instability (Patton et al., 1995 )), and to a lesser extent, to motor (motor impulsiveness and perseverance) and non-planning (self-control and cognitive complexity) impulsiveness. Going further into this dynamic, the mediating role of attentional impulsiveness in the relationship between sleep and interpersonal functioning is substantiated by both the aforementioned physiological consequences associated with disordered sleep behaviors, as well as the importance of attention in interpersonal functioning (Capozzi and Ristic, 2018 ; Pons et al., 2019 ; Taylor et al., 2016 ; Wolf et al., 2014 ). While these findings, based on the specific directionality investigated in our study, further highlight the importance of proper sleep, in order to minimize/avoid negative repercussions associated with both emotion and attention, which have broad implications on human behavior in general, it is necessary to be mindful of the multidirectional relationship between sleep and both emotion and attention (including in association with interpersonal functioning and impulsivity). This multidirectionality is potentially evident in regard to attention-deficit/hyperactivity disorder (ADHD), where ADHD can lead to disturbed sleep (in addition to emotional dysregulation and reduced attention) or, conversely, disturbed sleep can potentially lead to similar behaviors and symptoms to that of ADHD (Hvolby, 2015 ; Soler-Gutierrez et al., 2023 ). Thus, while additional analyses could investigate the alternate relationships between these variables (e.g., the potential mediating role of sleep in the relationship between impulsivity and interpersonal functioning), the implications of our findings and these complex relationships between the variables addressed above highlight the necessity, including at the clinical level, of ensuring that the primary problem is identified and addressed in order to avoid ineffective/inappropriate treatment strategies.

Role of sleep, attention and interpersonal functioning in daily life/functioning

Within the student population.

While academics should be the fundamental objective of student life, they do not occur in a vacuum. As per society in general, students are functioning within a social structure that forms the university community and consists of social interactions. As indicated by our findings, students’ interpersonal functioning was negatively impacted by poor sleep quality. Previous literature pertaining to university students has suggested that a lack of social interactions or the presence of negative social interactions have been associated with increased negative psychological consequences (e.g., increased perceived stress, increased proneness to boredom, increased negative emotional wellbeing) (e.g, Dumitrache et al., 2021 ; Fiori and Consedine, 2013 ). In addition, other research has indicated the beneficial role of positive social interactions, including indirectly through their influence on other positive behaviors such as physical activity, which has also been shown to be beneficial to well-being (e.g., Kawachi and Berkman, 2001 ; Reifman and Dunkel-Schetter, 1990 ; Vankim and Nelson, 2013 ). While the impact of social interactions on well-being has been recognized extensively in the literature, its relevance became particularly pronounced during COVID-19, a time when many individuals were deprived of in-person social interactions, contributing to a decline in mental health (e.g., Bzdok and Dunbar, 2020 ; Dumitrache et al., 2021 ).

Another fundamental aspect pertaining to the university student population that warrants attention is the impact of the students’ lifestyle (e.g., quality of sleep, involvement in sports, etc.) on their academics. Within the student population, a significant amount of literature has indicated the importance of sleep and proper attention in academic functioning and success (e.g., Curcio et al., 2006 ; Henning et al., 2022 ; Jalali et al., 2020 ; Okano et al., 2019 ). This is of particular concern given the levels of inappropriate sleep, in addition to its consequences on attention, reported both in the current study, as well as other contemporary research (Chua et al., 2017 ; Massar et al., 2019 ; Mbous et al., 2022 ), as addressed above.

Beyond the student population

While our study addressed sleep, impulsivity, and interpersonal functioning specifically in the university student population with its specific characteristics (e.g., studying, learning) and lifestyle (e.g., new social circles, sleep-habit changes, dietary changes), the findings may (taking into consideration certain limitations, e.g., socioeconomic status, life events, etc.) also have broader implications on the general population. In this regard, executive functioning, attention, interpersonal relationships, and the impact of sleep on such behaviors, have the potential to significantly influence numerous common day-to-day human behaviors and tasks (e.g., reading, writing, etc.), including in the context of various work settings (e.g., hands-on labor, operation of equipment, etc.) (Brossoit et al., 2019 ; Litwiller et al., 2017 ; Pilcher and Morris, 2020 ). Thus, as is reflected in the literature, it is not just the student population that suffers the consequences resulting from inappropriate/poor sleep. Ultimately, despite the differences that naturally exist across various social environments (e.g., school, workplace, home), there is a fundamental modus operandi in human physiological and behavioral functioning that is impacted by sleep and therefore, in reality, the implications of our findings are likely generalizable well beyond the student population.

Addressing the issue: the need to focus on the problem

As previously mentioned, pharmacological agents can be utilized to address the negative symptoms experienced as a result of poor/inappropriate sleep. However, these drugs cannot and do not replace the role of sleep as an essential innate physiological behavior. Inappropriate use of pharmacological agents (including in beverages containing caffeine such as, energy drinks, coffee, etc.) may lead to negative repercussions including, but not limited to, an increased propensity for error, including in critical/crucial situations (e.g., surgeon errors, adverse performance and safety outcomes in police officers, etc.) despite the acute relief of the symptoms (Aggarwal et al., 2011 ; Ogeil et al., 2018 ). Thus, seeking to relieve symptoms ( reactive approach) may be necessary when a problem is already present. However, the long-term goal of any approach must be focused on addressing the root  (fundamental underlying cause) of the issue (e.g., Riera-Sampol et al., 2022 ).

In the context of our study, impulsive behavior was an important factor in the relationships observed. Impulsive behavior underlies negative mental health and a broad spectrum of psychiatric disorders (e.g., Griffin et al., 2018 ; Smith et al., 2019 ; Tangney et al., 2004 ). In contrast, impulse control (i.e., increasing self-control), including its promotion and utilization in therapy (e.g., CBT), has been previously shown to be of benefit in a broad spectrum of behaviors (Smith et al., 2019 ; Tangney et al., 2004 ). Thus, encouraging principled behaviors that promote self-discipline/self-control has the potential to reduce negative (e.g., impulsive) and contribute to positive behaviors across a broad spectrum of life circumstances, including, but not limited to, interpersonal relationships, academics and avoidance of destructive behaviors, such as alcohol abuse (Claver et al., 2020 ; Rogus, 1985 ; Shi and Qu, 2022 ; Tangney et al., 2004 ).

The necessity for principled behaviors in order to achieve success, including in academia, is not a new concept, remembering that university life reflects, in many ways, a microcosm of society. Self-discipline, through its influence on decision-making, ultimately impacts common daily behaviors, such as sleep, diet and general hygiene (Hershner and O'Brien, 2018 ; Sertillanges, 1946 (1998)), and requires a consistent effort over time, through potential modifications of behavior, to achieve one’s ultimate goal (e.g., improving sleep) (Duckworth et al., 2007 ; Hagger and Hamilton, 2019 ; Li and Li, 2021 ). Such perseverance (sometimes referred to as “grit” in the literature, or “perseverance and passion for long-term goals” (Duckworth et al., 2007 ) has long term implications, with an ultimate potential outcome of improved overall general and psychological well-being (Duckworth et al., 2007 ; Lee et al., 2023 ; Verberg et al., 2019 ).

Underlying principled behaviors, self-discipline, and perseverance is motivation, which drives personal initiative and, in turn, is dependent on personal autonomy (Cook and Artino, 2016 ; Patel and Thatcher, 2012 ). However, given the social nature of humans, and that personal autonomy does not exclude social interaction and community (including, but not limited to, family), but rather includes healthy social relationships while preserving the capacity for self-determination (Ryff, 2014 ; Vansteenkiste and Ryan, 2013 ), the cooperation of individuals is necessary in order to maximize the impact of self-discipline. While personal effort is imperative on the side of the individual (e.g., student, employee, etc.) to be self-disciplined (e.g., getting proper sleep, studying, etc.), community support and reinforcement (e.g., in policy-making, in education, by university administration, professors, parents, etc.) is essential. In effect, self-discipline needs to become “everyone’s reality” (Rogus, 1985 ).

Limitations

The primary limitation of our study is that associations with measures of mental health such as depression, anxiety and stress cannot be made given a direct measure of mental health (e.g., DASS-21) was not included. However, the study did investigate impulsivity, which is an underlying characteristic of various mental health disorders (e.g., Chamorro et al., 2012 ; Moeller et al., 2001 ) and reflects a dysfunctional corticolimbic dynamic which is documented extensively clinically and in the scientific literature as underlying psychopathology/psychiatric disorders (e.g., Kovner et al., 2019 ; Spikman et al., 2000 ). It is predicted, given previous literature (Alonzo et al., 2021 ; Zou et al., 2020 ), that had direct mental health measures been included, individuals reporting poor sleep quality (as measured by the PSQI) would have also reported more negative mental health, similar to what was observed with impulsivity. Related, while psychopathologies that have the potential to impact the variables investigated in this study, such as ADHD (Becker et al., 2018 ; Sodano et al., 2021 ) or bipolar disorder (Palagini et al., 2019 ), were not included as exclusion criteria or covariates, this limitation needs to be considered in the context of the multidirectional relationship of the variables involved in sleep, including its relationship to ADHD as previously discussed in Sect. "  Interpersonal Functioning, Sleep and Executive Function ".

A consideration pertaining to the interpretation of the results and their implications is the necessity to also consider the limitations (which continue to be debated, most especially in the field of psychology) of any research design and the optimal statistical methods utilized. For example, we utilized t-tests to compare sex differences for the PSQI, BIS-11, FIAT-Q-SF subscales individually. While we chose this test because of the fact that each subscale is a separate entity and we were not comparing between the subscales within a specific scale, the reader needs to consider the potential of an inflated Type-I error rate resulting from this methodology in the interpretation of these results. Additional consideration becomes particularly relevant in relation to the more complex analyses such as mediation analysis. However, while caution is always necessary when implying causation, the reality of the complexity of human behavior and the multidirectional and multicomponent relationships that most investigated human behaviors involve (i.e. the rarity of unidirectional relationships) need to be considered, as well as the consistency and logic of any findings with those of existing research and human experience, which we sought to consider in the discussion of our findings.

Additionally, while our study consisted of a sample size representative of our population, a larger sample size or alternative design (e.g., longitudinal study) could potentially have provided further clarity on certain statistical outcomes that were bordering on significance such as the impulsivity subscales in relation to their potential mediating role. Moreover, while our study included a sample from a single university student population, the findings should be considered in the context of the fundamental consistencies that exist within human behavior across cultures/societies, and the congruity between the findings in our samples and other scientific literature pertaining to university students and the general population. Thus, similar results would be expected in studies investigating other populations, including other university student samples.

An additional point for consideration is that while the scientific literature indicates negative repercussions of COVID-19 on individual’s sleep/sleep quality (e.g., Alimoradi et al., 2021 ; Alqahtani et al., 2022 ; Ferreira-Souza et al., 2023 ; Fiorillo et al., 2020 ; Mandelkorn et al., 2021 ; Morin and Carrier, 2021 ; Son et al., 2020 ), given the data for this study was collected in 2022, our study cannot provide any direct link to COVID-19 measures, although such an impact cannot be totally dismissed, as also reflected in our previous findings (Emmerton et al., 2024 ). Additionally, given that this was not a longitudinal study, it is also not possible to make comparisons to the pre-COVID-19 time period. Of note, at the time of the survey administration, the dynamics associated with lockdowns, mask mandates, etc. had been substantially relaxed (Abbasi, 2022 ; Ballotpedia, 2022 ).

The levels of poor sleep quality and impulsivity reported both in our study and previous literature are of particular concern, given their potential to negatively impact an individual’s physiological/physical and psychological well-being, which can ultimately influence behaviors, including interpersonal functioning (as indicated by our findings), with the potential to further impact the well-being of the individual. This is particularly important given the continued deterioration in mental health despite the increased resources being made available. Overall, our findings suggest that sleep quality and its relationship with impulsivity, specifically attentional impulsivity, have the potential to impact an individual’s interpersonal functioning.

As appears to be the case in most circumstances in life, in order to achieve overall well-being, when an issue arises, it is ideal that a long-term approach that addresses the root of the problem is considered, rather than solely targeting, in the short-term, the phenotypic “symptoms” resulting from the primary issue. In regard to the current findings, rather than simply seeking to minimize the negative consequences associated with lower interpersonal functioning, it would appear to be more beneficial to maximize preventative constructive behaviors (such as improving sleep quality and minimizing impulsivity, i.e., addressing the root of the issue) that build character/virtue and strengthen the individual, including through self-discipline and perseverance.

Availability of data and materials

The data underlying this article will be shared on reasonable request to the corresponding author.

Abbreviations

Pittsburgh Sleep Quality Index

Barratt Impulsiveness Scale Version 11

Attentional Impulsiveness

Motor Impulsiveness

Non-planning Impulsiveness

Functional Idiographic Assessment Template—Questionnaire—Short Form

Avoidance of Interpersonal Intimacy

Argumentativeness or Disagreement

Connection and Reciprocity

Conflict Aversion

Emotional Experience and Expression

Excessive Expressivity

aan Het Rot M, Moskowitz DS, Young SN. Impulsive behaviour in interpersonal encounters: associations with quarrelsomeness and agreeableness. Br J Psychol. 2015;106(1):152–61.

Article   PubMed   Google Scholar  

Abbasi E. State by State Face Mask Mandates 2022 Available from: https://leadingage.org/state-state-face-mask-mandates/ .

Abel T, Havekes R, Saletin JM, Walker MP. Sleep, plasticity and memory from molecules to whole-brain networks. Curr Biol. 2013;23(17):R774–88.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Aggarwal R, Mishra A, Crochet P, Sirimanna P, Darzi A. Effect of caffeine and taurine on simulated laparoscopy performed following sleep deprivation. Br J Surg. 2011;98(11):1666–72.

Article   PubMed   CAS   Google Scholar  

Albers HE. The regulation of social recognition, social communication and aggression: vasopressin in the social behavior neural network. Horm Behav. 2012;61(3):283–92.

Albqoor MA, Shaheen AM. Sleep quality, sleep latency, and sleep duration: a national comparative study of university students in Jordan. Sleep Breath. 2021;25(2):1147–54.

Alimoradi Z, Brostrom A, Tsang HWH, Griffiths MD, Haghayegh S, Ohayon MM, et al. Sleep problems during COVID-19 pandemic and its’ association to psychological distress: a systematic review and meta-analysis. EClinicalMedicine. 2021;36:100916.

Article   PubMed   PubMed Central   Google Scholar  

Almojali AI, Almalki SA, Alothman AS, Masuadi EM, Alaqeel MK. The prevalence and association of stress with sleep quality among medical students. J Epidemiol Glob Health. 2017;7(3):169–74.

Alonzo R, Hussain J, Stranges S, Anderson KK. Interplay between social media use, sleep quality, and mental health in youth: a systematic review. Sleep Med Rev. 2021;56:101414.

Alotaibi AD, Alosaimi FM, Alajlan AA, Bin Abdulrahman KA. The relationship between sleep quality, stress, and academic performance among medical students. J Family Community Med. 2020;27(1):23–8.

Alqahtani JS, AlRabeeah SM, Aldhahir AM, Siraj R, Aldabayan YS, Alghamdi SM, Alqahtani AS, Alsaif SS, Naser AY, Alwafi H. Sleep Quality, Insomnia, Anxiety, Fatigue, Stress, Memory and Active Coping during the COVID-19 Pandemic. Int J Environ Res Public Health. 2022;19(9);4940. https://doi.org/10.3390/ijerph19094940 .

Asif N, Iqbal R, Nazir CF. Human immune system during sleep. Am J Clin Exp Immunol. 2017;6(6):92–6.

PubMed   PubMed Central   Google Scholar  

Ballotpedia. State-level mask requirements in response to the coronavirus (COVID-19) pandemic, 2020–2022. 2022. Available from: https://ballotpedia.org/State-level_mask_requirements_in_response_to_the_coronavirus_(COVID-19)_pandemic,_2020-2022 .

Google Scholar  

Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173–82.

Beattie L, Kyle SD, Espie CA, Biello SM. Social interactions, emotion and sleep: a systematic review and research agenda. Sleep Med Rev. 2015;24:83–100.

Becker SP, Jarrett MA, Luebbe AM, Garner AA, Burns GL, Kofler MJ. Sleep in a large, multi-university sample of college students: sleep problem prevalence, sex differences, and mental health correlates. Sleep Health. 2018;4(2):174–81.

Beiter R, Nash R, McCrady M, Rhoades D, Linscomb M, Clarahan M, et al. The prevalence and correlates of depression, anxiety, and stress in a sample of college students. J Affect Disord. 2015;173:90–6.

Ben Simon E, Vallat R, Rossi A, Walker MP. Sleep loss leads to the withdrawal of human helping across individuals, groups, and large-scale societies. PLoS Biol. 2022;20(8):e3001733.

Benington JH, Heller HC. Restoration of brain energy metabolism as the function of sleep. Prog Neurobiol. 1995;45(4):347–60.

Brossoit RM, Crain TL, Leslie JJ, Hammer LB, Truxillo DM, Bodner TE. The effects of sleep on workplace cognitive failure and safety. J Occup Health Psychol. 2019;24(4):411–22.

Bruffaerts R, Mortier P, Kiekens G, Auerbach RP, Cuijpers P, Demyttenaere K, et al. Mental health problems in college freshmen: prevalence and academic functioning. J Affect Disord. 2018;225:97–103.

Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

Bzdok D, Dunbar RIM. The neurobiology of social distance. Trends Cogn Sci. 2020;24(9):717–33.

Capozzi F, Ristic J. How attention gates social interactions. Ann N Y Acad Sci. 2018. Advance online publication.  https://doi.org/10.1111/nyas.13854 .

Chamorro J, Bernardi S, Potenza MN, Grant JE, Marsh R, Wang S, et al. Impulsivity in the general population: a national study. J Psychiatr Res. 2012;46(8):994–1001.

Chatburn A, Coussens S, Kohler MJ. Resiliency as a mediator of the impact of sleep on child and adolescent behavior. Nat Sci Sleep. 2013;6:1–9.

Chattu VK, Manzar MD, Kumary S, Burman D, Spence DW, Pandi-Perumal SR. The global problem of insufficient sleep and its serious public health implications. Healthcare (Basel). 2018;7(1).

Cheng W, Rolls E, Gong W, Du J, Zhang J, Zhang XY, et al. Sleep duration, brain structure, and psychiatric and cognitive problems in children. Mol Psychiatry. 2021;26(8):3992–4003.

Chiu K, Clark DM, Leigh E. Prospective associations between peer functioning and social anxiety in adolescents: a systematic review and meta-analysis. J Affect Disord. 2021;279:650–61.

Chua EC, Fang E, Gooley JJ. Effects of total sleep deprivation on divided attention performance. PLoS ONE. 2017;12(11):e0187098.

Claver F, Martinez-Aranda LM, Conejero M, Gil-Arias A. Motivation, Discipline, and academic performance in physical education: a holistic approach from achievement goal and self-determination theories. Front Psychol. 2020;11:1808.

Cook DA, Artino AR Jr. Motivation to learn: an overview of contemporary theories. Med Educ. 2016;50(10):997–1014.

Crocker J, Canevello A, Brown AA. Social motivation: costs and benefits of selfishness and otherishness. Annu Rev Psychol. 2017;68:299–325.

Curcio G, Ferrara M, De Gennaro L. Sleep loss, learning capacity and academic performance. Sleep Med Rev. 2006;10(5):323–37.

Darrow SM, Callaghan GC, Bonow JT, Follette WC. The Functional Idiographic Assessment Template-Questionnaire (FIAT-Q): initial psychometric properties. J Contextual Behav Sci. 2014;3(2):124–35.

Dattilo M, Antunes HK, Medeiros A, Monico Neto M, Souza HS, Tufik S, et al. Sleep and muscle recovery: endocrinological and molecular basis for a new and promising hypothesis. Med Hypotheses. 2011;77(2):220–2.

DeBono A, Shmueli D, Muraven M. Rude and inappropriate: the role of self-control in following social norms. Pers Soc Psychol Bull. 2011;37(1):136–46.

Dias C, Costa D, Sousa T, Castelhano J, Figueiredo V, Pereira AC, et al. A neuronal theta band signature of error monitoring during integration of facial expression cues. PeerJ. 2022;10:e12627.

Dietch JR, Taylor DJ, Sethi K, Kelly K, Bramoweth AD, Roane BM. Psychometric evaluation of the PSQI in U.S. college students. J Clin Sleep Med. 2016;12(8):1121–9.

Dinges DF, Pack F, Williams K, Gillen KA, Powell JW, Ott GE, et al. Cumulative sleepiness, mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restricted to 4–5 hours per night. Sleep. 1997;20(4):267–77.

PubMed   CAS   Google Scholar  

Dinis J, Braganca M. Quality of sleep and depression in college students: a systematic review. Sleep Sci. 2018;11(4):290–301.

Dixon LJ, Tull MT, Lee AA, Kimbrel NA, Gratz KL. The role of emotion-driven impulse control difficulties in the relation between social anxiety and aggression. J Clin Psychol. 2017;73(6):722–32.

Dorrian J, Centofanti S, Smith A, McDermott KD. Self-regulation and social behavior during sleep deprivation. Prog Brain Res. 2019;246:73–110.

Duckworth AL, Peterson C, Matthews MD, Kelly DR. Grit: perseverance and passion for long-term goals. J Pers Soc Psychol. 2007;92(6):1087–101.

Dumitrache L, Stanculescu E, Nae M, Dumbraveanu D, Simion G, Talos AM, et al. Post-lockdown effects on students' mental health in Romania: perceived stress, missing daily social interactions, and boredom proneness. Int J Environ Res Public Health. 2021;18(16).

Emmerton RW, Camilleri C, Sammut S. Continued deterioration in university student mental health: Inevitable decline or skirting around the deeper problems? J Affect Disord Rep. 2024;15:100691.

Article   Google Scholar  

Ferreira-Souza LF, Julianelli-Pecanha M, Coelho-Oliveira AC, da Silva Bahia CMC, Paineiras-Domingos LL, Reis-Silva A, et al. Impacts of COVID-19 pandemic on sleep quality evaluated by wrist actigraphy: a systematic review. J Clin Med. 2023;12(3).

Fetzer Institute / National Institute on Aging Working Group. Multidimensional measurement of religiousness/spirituality for use in health research. Kalamazoo: MI: Fetzer Institute; 2003.

Fiori KL, Consedine NS. Positive and negative social exchanges and mental health across the transition to college. J Soc Pers Relat. 2013;30(7):920–41.

Fiorillo A, Sampogna G, Giallonardo V, Del Vecchio V, Luciano M, Albert U, et al. Effects of the lockdown on the mental health of the general population during the COVID-19 pandemic in Italy: results from the COMET collaborative network. Eur Psychiatry. 2020;63(1):e87.

Gaine ME, Chatterjee S, Abel T. Sleep deprivation and the epigenome. Front Neural Circuits. 2018;12:14.

Gilboa-Schechtman E, Shachar-Lavie I. More than a face: a unified theoretical perspective on nonverbal social cue processing in social anxiety. Front Hum Neurosci. 2013;7:904.

Gillett G, Watson G, Saunders KE, McGowan NM. Sleep and circadian rhythm actigraphy measures, mood instability and impulsivity: a systematic review. J Psychiatr Res. 2021;144:66–79.

Goldstein AN, Walker MP. The role of sleep in emotional brain function. Annu Rev Clin Psychol. 2014;10:679–708.

Gordon AM, Carrillo B, Barnes CM. Sleep and social relationships in healthy populations: a systematic review. Sleep Med Rev. 2021;57: 101428.

Griffin SA, Lynam DR, Samuel DB. Dimensional conceptualizations of impulsivity. Personal Disord. 2018;9(4):333–45.

Hagger MS, Hamilton K. Grit and self-discipline as predictors of effort and academic attainment. Br J Educ Psychol. 2019;89(2):324–42.

Harrison Y, Horne JA. The impact of sleep deprivation on decision making: a review. J Exp Psychol Appl. 2000;6(3):236–49.

Hawkley LC, Cacioppo JT. Loneliness matters: a theoretical and empirical review of consequences and mechanisms. Ann Behav Med. 2010;40(2):218–27.

Hayashi Y, Rivera EA, Modico JG, Foreman AM, Wirth O. Texting while driving, executive function, and impulsivity in college students. Accid Anal Prev. 2017;102:72–80.

Hayashi Y, Washio Y. Text-message dependency, executive function, and impulsivity in college students: a cluster analysis. Cyberpsychol Behav Soc Netw. 2020;23(11):794–9.

Hefner J, Eisenberg D. Social support and mental health among college students. Am J Orthopsychiatry. 2009;79(4):491–9.

Henning C, Summerfeldt LJ, Parker JDA. ADHD and academic success in university students: the important role of impaired attention. J Atten Disord. 2022;26(6):893–901.

Hershner S, O’Brien LM. The impact of a randomized sleep education intervention for college students. J Clin Sleep Med. 2018;14(3):337–47.

Hudson AN, Van Dongen HPA, Honn KA. Sleep deprivation, vigilant attention, and brain function: a review. Neuropsychopharmacology. 2020;45(1):21–30.

Huttermann S, Helsen WF, Put K, Memmert D. Does visual attention impact on decision making in complex dynamic events? J Sport Exerc Psychol. 2018;40(3):163–6.

Hvolby A. Associations of sleep disturbance with ADHD: implications for treatment. Atten Defic Hyperact Disord. 2015;7(1):1–18.

Jalali R, Khazaei H, Paveh BK, Hayrani Z, Menati L. The effect of sleep quality on students’ academic achievement. Adv Med Educ Pract. 2020;11:497–502.

Jentsch JD, Ashenhurst JR, Cervantes MC, Groman SM, James AS, Pennington ZT. Dissecting impulsivity and its relationships to drug addictions. Ann N Y Acad Sci. 2014;1327:1–26.

Jones CB, Meier MH, Corbin WE, Chassin L. Adolescent executive cognitive functioning and trait impulsivity as predictors of young-adult risky drinking and alcohol-related problems. Psychol Addict Behav. 2021;35(2):187–98.

Kawachi I, Berkman LF. Social ties and mental health. J Urban Health. 2001;78(3):458–67.

Kechter A, Leventhal AM. Longitudinal Association of Sleep Problems and Distress Tolerance During Adolescence. Behav Med. 2019;45(3):240–8.

Killgore WD, Balkin TJ, Wesensten NJ. Impaired decision making following 49 h of sleep deprivation. J Sleep Res. 2006;15(1):7–13.

Kim TW, Jeong JH, Hong SC. The impact of sleep and circadian disturbance on hormones and metabolism. Int J Endocrinol. 2015;2015: 591729.

Kovner R, Oler JA, Kalin NH. Cortico-Limbic Interactions Mediate Adaptive and Maladaptive Responses Relevant to Psychopathology. Am J Psychiatry. 2019;176(12):987–99.

Krakowski M. Violence and serotonin: influence of impulse control, affect regulation, and social functioning. J Neuropsychiatry Clin Neurosci. 2003;15(3):294–305.

Lee D, Reasoner K, Davidson C, Pennings JS, Lee DH. The relationships between grit, burnout, and demographic characteristics in medical students. Psychol Rep. 2023;126(5):2511–29.

Leigh-Hunt N, Bagguley D, Bash K, Turner V, Turnbull S, Valtorta N, et al. An overview of systematic reviews on the public health consequences of social isolation and loneliness. Public Health. 2017;152:157–71.

Leproult R, Van Cauter E. Role of sleep and sleep loss in hormonal release and metabolism. Endocr Dev. 2010;17:11–21.

Lewis C, Carpendale JI. Introduction: Links between social interaction and executive function. New Dir Child Adolesc Dev. 2009;2009(123):1–15.

Li J, Li Y. The role of grit on students' academic success in experiential learning context. Front Psychol. 2021;12.

Libedinsky C, Smith DV, Teng CS, Namburi P, Chen VW, Huettel SA, et al. Sleep deprivation alters valuation signals in the ventromedial prefrontal cortex. Front Behav Neurosci. 2011;5:70.

Limone P, Toto GA. Factors that predispose undergraduates to mental issues: a cumulative literature review for future research perspectives. Front Public Health. 2022;10: 831349.

Lipson SK, Lattie EG, Eisenberg D. Increased rates of mental health service utilization by U.S. college students: 10-year population-level trends (2007–2017). Psychiatr Serv. 2019;70(1):60–3.

Litwiller B, Snyder LA, Taylor WD, Steele LM. The relationship between sleep and work: A meta-analysis. J Appl Psychol. 2017;102(4):682–99.

Loades ME, Chatburn E, Higson-Sweeney N, Reynolds S, Shafran R, Brigden A, et al. Rapid systematic review: the impact of social isolation and loneliness on the mental health of children and adolescents in the context of COVID-19. J Am Acad Child Adolesc Psychiatry. 2020;59(11):1218-39 e3.

Logue SF, Gould TJ. The neural and genetic basis of executive function: attention, cognitive flexibility, and response inhibition. Pharmacol Biochem Behav. 2014;123:45–54.

Lund HG, Reider BD, Whiting AB, Prichard JR. Sleep patterns and predictors of disturbed sleep in a large population of college students. J Adolesc Health. 2010;46(2):124–32.

Madjar N, Chubarov E, Zalsman G, Weiser M, Shoval G. Social skills, executive functioning and social engagement. Schizophr Res Cogn. 2019;17:100137.

Mandelkorn U, Genzer S, Choshen-Hillel S, Reiter J, Meira ECM, Hochner H, et al. Escalation of sleep disturbances amid the COVID-19 pandemic: a cross-sectional international study. J Clin Sleep Med. 2021;17(1):45–53.

Maquet P. The role of sleep in learning and memory. Science. 2001;294(5544):1048–52.

Massar SAA, Lim J, Sasmita K, Chee MWL. Sleep deprivation increases the costs of attentional effort: performance, preference and pupil size. Neuropsychologia. 2019;123:169–77.

Mbous YPV, Nili M, Mohamed R, Dwibedi N. Psychosocial correlates of insomnia among college students. Prev Chronic Dis. 2022;19:E60.

Moeller FG, Barratt ES, Dougherty DM, Schmitz JM, Swann AC. Psychiatric aspects of impulsivity. Am J Psychiatry. 2001;158(11):1783–93.

Mohammed AR, Kosonogov V, Lyusin D. Is emotion regulation impacted by executive functions? An experimental study. Scand J Psychol. 2022;63(3):182–90.

Mong JA, Cusmano DM. Sex differences in sleep: impact of biological sex and sex steroids. Philos Trans R Soc Lond B Biol Sci. 2016;371(1688):20150110.

Morin CM, Carrier J. The acute effects of the COVID-19 pandemic on insomnia and psychological symptoms. Sleep Med. 2021;77:346–7.

Narvaez JC, Magalhaes PV, Trindade EK, Vieira DC, Kauer-Sant’anna M, Gama CS, et al. Childhood trauma, impulsivity, and executive functioning in crack cocaine users. Compr Psychiatry. 2012;53(3):238–44.

Ogeil RP, Barger LK, Lockley SW, O’Brien CS, Sullivan JP, Qadri S, et al. Cross-sectional analysis of sleep-promoting and wake-promoting drug use on health, fatigue-related error, and near-crashes in police officers. BMJ Open. 2018;8(9):e022041.

Okano K, Kaczmarzyk JR, Dave N, Gabrieli JDE, Grossman JC. Sleep quality, duration, and consistency are associated with better academic performance in college students. NPJ Sci Learn. 2019;4:16.

Palagini L, Cipollone G, Masci I, Caruso D, Paolilli F, Perugi G, et al. Insomnia symptoms predict emotional dysregulation, impulsivity and suicidality in depressive bipolar II patients with mixed features. Compr Psychiatry. 2019;89:46–51.

Parks KMA, Cardy JEO, Woynaroski TG, Sehl CG, Stevenson RA. Investigating the role of inattention and/or hyperactivity/impulsivity in language and social functioning using a dimensional approach. J Commun Disord. 2021;89:106036.

Patel PC, Thatcher SMB. Sticking it out. J Manag. 2012;40(7):1932–79.

Patton JH, Stanford MS, Barratt ES. Factor structure of the barratt impulsiveness scale. J Clin Psychol. 1995;51(6):768–74.

Pigeon WR, Pinquart M, Conner K. Meta-analysis of sleep disturbance and suicidal thoughts and behaviors. J Clin Psychiatry. 2012;73(9):e1160–7.

Pilcher JJ, Morris DM. Sleep and organizational behavior: implications for workplace productivity and safety. Front Psychol. 2020;11:45.

Pons F, Bosch L, Lewkowicz DJ. Twelve-month-old infants’ attention to the eyes of a talking face is associated with communication and social skills. Infant Behav Dev. 2019;54:80–4.

Potenza MN, de Wit H. Control yourself: alcohol and impulsivity. Alcohol Clin Exp Res. 2010;34(8):1303–5.

Ramon-Arbues E, Granada-Lopez JM, Martinez-Abadia B, Echaniz-Serrano E, Anton-Solanas I, Jerue BA. The association between diet and sleep quality among spanish university students. Nutrients. 2022;14(16).

Rangelov D, Mattingley JB. Evidence accumulation during perceptual decision-making is sensitive to the dynamics of attentional selection. Neuroimage. 2020;220:117093.

Rasch B, Born J. About sleep’s role in memory. Physiol Rev. 2013;93(2):681–766.

Reichl D, Heindl B, Distler AL, Steins-Loeber S. Attentional impulsivity accounts for the association of antisociality with craving and mental health problems in incarcerated individuals with substance dependence. Int J Prison Health. 2023. https://doi.org/10.1108/IJPH-03-2022-0023 .

Reifman A, Dunkel-Schetter C. Stress, structural social support, and well-being in university students. J Am Coll Health. 1990;38(6):271–7.

Reynolds BW, Basso MR, Miller AK, Whiteside DM, Combs D. Executive function, impulsivity, and risky behaviors in young adults. Neuropsychology. 2019;33(2):212–21.

Riera-Sampol A, Rodas L, Martinez S, Moir HJ, Tauler P. Caffeine intake among undergraduate students: sex differences, sources, motivations, and associations with smoking status and self-reported sleep quality. Nutrients. 2022;14(8).

Roeser KA, Somers CL, Mangus LR. Emerging adults’ risk-taking behaviors: personal and social predictors. J Adult Dev. 2018;26(4):256–65.

Rogus JF. Promoting Self-discipline: a comprehensive approach. Theory Pract. 1985;24(4):271–6.

Ruz M, Tudela P. Emotional conflict in interpersonal interactions. Neuroimage. 2011;54(2):1685–91.

Ryff CD. Psychological well-being revisited: advances in the science and practice of eudaimonia. Psychother Psychosom. 2014;83(1):10–28.

Sa J, Samuel T, Chaput J-P, Chung J, Grigsby-Toussaint DS, Lee J. Sex and racial/ethnic differences in sleep quality and its relationship with body weight status among US college students. J Am Coll Health. 2019;68(7):704–11.

Sanz-Arigita E, Daviaux Y, Joliot M, Dilharreguy B, Micoulaud-Franchi JA, Bioulac S, et al. Brain reactivity to humorous films is affected by insomnia. Sleep. 2021;44(9).

Schlarb AA, Bihlmaier I, Velten-Schurian K, Poets CF, Hautzinger M. Short- and long-term effects of CBT-I in groups for school-age children suffering from chronic insomnia: the kiss-program. Behav Sleep Med. 2018;16(4):380–97.

Scott AJ, Webb TL, Martyn-St James M, Rowse G, Weich S. Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials. Sleep Med Rev. 2021;60:101556.

Sepulveda-Loyola W, Rodriguez-Sanchez I, Perez-Rodriguez P, Ganz F, Torralba R, Oliveira DV, et al. Impact of Social Isolation Due to COVID-19 on Health in Older People: Mental and Physical Effects and Recommendations. J Nutr Health Aging. 2020;24(9):938–47.

Sertillanges AG. The intellectual life: Its spirit, conditions, methods: The Catholic University of America Press; 1946 (1998).

Shi Y, Qu S. The effect of cognitive ability on academic achievement: The mediating role of self-discipline and the moderating role of planning. Front Psychol. 2022;13:1014655.

Shrout PE, Bolger N. Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychol Methods. 2002;7(4):422–45.

Simoes-Franklin C, Hester R, Shpaner M, Foxe JJ, Garavan H. Executive function and error detection: the effect of motivation on cingulate and ventral striatum activity. Hum Brain Mapp. 2010;31(3):458–69.

Smith T, Panfil K, Bailey C, Kirkpatrick K. Cognitive and behavioral training interventions to promote self-control. J Exp Psychol Anim Learn Cogn. 2019;45(3):259–79.

Sodano SM, Tamulonis JP, Fabiano GA, Caserta AM, Hulme KF, Hulme KL, et al. Interpersonal problems of young adults with and without attention-deficit/hyperactivity disorder. J Atten Disord. 2021;25(4):562–71.

Soler-Gutierrez AM, Perez-Gonzalez JC, Mayas J. Evidence of emotion dysregulation as a core symptom of adult ADHD: a systematic review. PLoS ONE. 2023;18(1):e0280131.

Son C, Hegde S, Smith A, Wang X, Sasangohar F. Effects of COVID-19 on college students’ mental health in the united states: interview survey study. J Med Internet Res. 2020;22(9):e21279.

Spikman JM, Deelman BG, van Zomeren AH. Executive functioning, attention and frontal lesions in patients with chronic CHI. J Clin Exp Neuropsychol. 2000;22(3):325–38.

Stanford MS, Mathias CW, Dougherty DM, Lake SL, Anderson NE, Patton JH. Fifty years of the Barratt Impulsiveness Scale: an update and review. Personality Individ Differ. 2009;47(5):385–95.

Swann AC, Lijffijt M, Lane SD, Steinberg JL, Moeller FG. Interactions between bipolar disorder and antisocial personality disorder in trait impulsivity and severity of illness. Acta Psychiatr Scand. 2010;121(6):453–61.

Tangney JP, Baumeister RF, Boone AL. High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers. 2004;72(2):271–324.

Taylor CT, Cross K, Amir N. Attentional control moderates the relationship between social anxiety symptoms and attentional disengagement from threatening information. J Behav Ther Exp Psychiatry. 2016;50:68–76.

Taylor HO, Taylor RJ, Nguyen AW, Chatters L. Social isolation, depression, and psychological distress among older adults. J Aging Health. 2018;30(2):229–46.

Tempesta D, Socci V, De Gennaro L, Ferrara M. Sleep and emotional processing. Sleep Med Rev. 2018;40:183–95.

Thomas M, Sing H, Belenky G, Holcomb H, Mayberg H, Dannals R, et al. Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. J Sleep Res. 2000;9(4):335–52.

Torales J, Kadhum M, Zarate G, Barrios I, Gonzalez I, Farrell SM, et al. Wellbeing and mental health among medical students in Paraguay. Int Rev Psychiatry. 2019;31(7–8):598–602.

Trauer JM, Qian MY, Doyle JS, Rajaratnam SM, Cunnington D. Cognitive behavioral therapy for chronic insomnia: a systematic review and meta-analysis. Ann Intern Med. 2015;163(3):191–204.

Tsai LL, Young HY, Hsieh S, Lee CS. Impairment of error monitoring following sleep deprivation. Sleep. 2005;28(6):707–13.

Twenge JM, Gentile B, DeWall CN, Ma D, Lacefield K, Schurtz DR. Birth cohort increases in psychopathology among young Americans, 1938–2007: a cross-temporal meta-analysis of the MMPI. Clin Psychol Rev. 2010;30(2):145–54.

van Gaal S, Lamme VA. Unconscious high-level information processing: implication for neurobiological theories of consciousness. Neuroscientist. 2012;18(3):287–301.

Vankim NA, Nelson TF. Vigorous physical activity, mental health, perceived stress, and socializing among college students. Am J Health Promot. 2013;28(1):7–15.

Vansteenkiste M, Ryan RM. On psychological growth and vulnerability: Basic psychological need satisfaction and need frustration as a unifying principle. J Psychother Integr. 2013;23(3):263–80.

Vedaa O, Erevik EK, Hysing M, Hayley AC, Sivertsen B. Insomnia, sleep duration and academic performance: a national survey of Norwegian college and university students. Sleep Med x. 2019;1:100005.

Verberg F, Helmond P, Otten R, Overbeek G. Mindset and perseverance of adolescents with intellectual disabilities: associations with empowerment, mental health problems, and self-esteem. Res Dev Disabil. 2019;91:103426.

Vyazovskiy VV, Delogu A. NREM and REM sleep: complementary roles in recovery after wakefulness. Neuroscientist. 2014;20(3):203–19.

Walker MP, Stickgold R. It’s practice, with sleep, that makes perfect: implications of sleep-dependent learning and plasticity for skill performance. Clin Sports Med. 2005;24(2):301–17, ix.

Whiting C, Bellaert N, Deveney C, Tseng W-L. Associations between sleep quality and irritability: testing the mediating role of emotion regulation. Pers Individ Dif. 2023;213.

Wolf RC, Philippi CL, Motzkin JC, Baskaya MK, Koenigs M. Ventromedial prefrontal cortex mediates visual attention during facial emotion recognition. Brain. 2014;137(Pt 6):1772–80.

Wu JQ, Appleman ER, Salazar RD, Ong JC. Cognitive behavioral therapy for insomnia comorbid with psychiatric and medical conditions: a meta-analysis. JAMA Intern Med. 2015;175(9):1461–72.

Wu JC, Gillin JC, Buchsbaum MS, Chen P, Keator DB, Khosla WuN, et al. Frontal lobe metabolic decreases with sleep deprivation not totally reversed by recovery sleep. Neuropsychopharmacology. 2006;31(12):2783–92.

Yoo SS, Gujar N, Hu P, Jolesz FA, Walker MP. The human emotional brain without sleep–a prefrontal amygdala disconnect. Curr Biol. 2007;17(20):R877–8.

Young CB, Nusslock R. Positive mood enhances reward-related neural activity. Soc Cogn Affect Neurosci. 2016;11(6):934–44.

Zohar D, Tzischinsky O, Epstein R, Lavie P. The effects of sleep loss on medical residents’ emotional reactions to work events: a cognitive-energy model. Sleep. 2005;28(1):47–54.

Zou P, Wang X, Sun L, Liu K, Hou G, Yang W, et al. Poorer sleep quality correlated with mental health problems in college students: a longitudinal observational study among 686 males. J Psychosom Res. 2020;136:110177.

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Acknowledgements

The authors would also like to acknowledge the assistance provided by Nathan Martin, Paul Gantz, Paul Marlowe, and Rose Prezzia and other members of the Franciscan University psychology club.

This research was funded by small, private individual, restricted donations made to Franciscan University as a 501(c)3 organization and specifically designated for work currently conducted by SS. Funding for the SurveyMonkey® subscription and gift card drawings was provided to SS by Franciscan University of Steubenville. Neither the donors nor the university had any role in the study design, data collection and analysis, interpretation of results, decision to publish, or preparation of the manuscript.

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Brian J. Farrell III, Robert W. Emmerton, Christina Camilleri & Stephen Sammut

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SS supervised the study. BJF and SS contributed to the conception, design and conducting of the study. SS, CC, RWE performed the statistical analyses and contributed to the writing of the first draft of the manuscript. All authors contributed to the revisions of the manuscript and read and approved the submitted version.

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Additional file 1: supplementary table 1. summary of demographic variables, 41606_2024_113_moesm2_esm.docx.

Additional file 2: Supplementary Table 2. Complete results for mediation pathways between sleep quality (PSQI) and interpersonal functioning (FIAT-Q-SF) with impulsivity (BIS-11: AttImp, MotImp, NpImp) as potential mediators. PSQI = Global PSQI; AttImp = Attentional impulsiveness; MotImp = Motor impulsiveness; NpImp = Non-planning impulsiveness; FIAT = Total FIAT-Q-SF

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Farrell, B.J., Emmerton, R.W., Camilleri, C. et al. Impulsivity mediates the relationship between sleep quality and interpersonal functioning: a cross-sectional study in a sample of university students. Sleep Science Practice 8 , 16 (2024). https://doi.org/10.1186/s41606-024-00113-8

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• Published: 27 September 2024

Nur77 protects the bladder urothelium from intracellular bacterial infection

• Christina A. Collins 1 ,
• Chevaughn Waller 2 ,
• Ekaterina Batourina 2 ,
• Lokesh Kumar 1 ,
• Cathy L. Mendelsohn   ORCID: orcid.org/0000-0002-4026-4073 2 &
• Nicole M. Gilbert   ORCID: orcid.org/0000-0003-4830-3257 1 , 3  

Nature Communications volume  15 , Article number:  8308 ( 2024 ) Cite this article

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• Urinary tract infection

Intracellular infections by Gram-negative bacteria are a significant global health threat. The nuclear receptor Nur77 (also called TR3, NGFI-B, or NR4A1) was recently shown to sense cytosolic bacterial lipopolysaccharide (LPS). However, the potential role for Nur77 in controlling intracellular bacterial infection has not been examined. Here we show that Nur77 protects against intracellular infection in the bladder by uropathogenic Escherichia coli (UPEC), the leading cause of urinary tract infections (UTI). Nur77 deficiency in mice promotes the formation of UPEC intracellular bacterial communities (IBCs) in the cells lining the bladder lumen, leading to persistent infection in bladder tissue. Conversely, treatment with a small-molecule Nur77 agonist, cytosporone B, inhibits invasion and enhances the expulsion of UPEC from human urothelial cells in vitro, and significantly reduces UPEC IBC formation and bladder infection in mice. Our findings reveal a new role for Nur77 in control of bacterial infection and suggest that pharmacologic agonism of Nur77 function may represent a promising antibiotic-sparing therapeutic approach for UTI.

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Ceragenin CSA-13 displays high antibacterial efficiency in a mouse model of urinary tract infection

Introduction.

The urinary tract is the second most common site of bacterial infection in humans, and uropathogenic Escherichia coli (UPEC) are the leading cause of UTI globally 1 . The high incidence of UTI results in sizeable health care costs, annually reaching billions of dollars globally and $2 billion in the USA alone 2 . Antibiotics remain the primary treatment option for UTI, precipitating the worldwide spread of antibiotic-resistant uropathogens. Even when initial treatment is effective, UTI remain highly recurrent. Nearly one in four women with an initial UTI will suffer recurrent UTI (rUTI) within 6 months, and up to 70% will experience rUTI within 1 year; half will have multiple recurrences over their lifetime 1 , 3 , and the yearly risk of recurrence in children is 19–22% 4 . Adult and pediatric patients with a history of rUTI are often given prophylactic antibiotics to ward off future infection, which predictably leads to infection with antibiotic-resistant pathogens 5 , 6 , 7 . New therapies targeting host factors that promote resistance to UTI without exacerbating the rise of antibiotic-resistant organisms would be highly useful.

During cystitis, several uropathogens, including UPEC, can invade and survive within uroepithelial cells where the bacteria can avoid antibiotic exposure 8 , 9 , 10 . Urothelial cells use several mechanisms to eliminate invading bacteria, including activating programmed cell death-mediated exfoliation 11 , 12 , 13 , 14 , inducing autophagy 15 , or by expulsion of internalized organisms 16 , 17 , 18 . When these measures fail, UPEC can escape into the cytosol and replicate to form large intracellular bacterial communities (IBCs), from which a subset of organisms eventually flux out of the cell to perpetuate the infection 19 , 20 . Intracellular bacteria have been detected within shed urothelial cells during UTI in adults 21 and children 22 . Smaller collections of intracellular UPEC can persist in urothelial cells for months following resolution of bacteriuria 23 , and these so-called quiescent intracellular reservoirs (QIRs) are considered a likely source of rUTI. In mice, uropathogen emergence can be triggered by bladder exposures to other urogenital bacteria 24 or upon catheterization 25 . In humans, up to two-third of sequential UTI episodes are caused by the same uropathogen strain, which is consistent with reservoir re-emergence. Bacteria have been detected in bladder biopsies from patients with a history of UTI, even after antibiotic treatment and confirmed clearance of bacteriuria 9 , 26 . Ideally, future UTI therapies would target UPEC intracellular infection.

The nuclear receptor Nur77, also known as NR4A1, TR3, or NGFI-B, is a transcription factor with important roles in inflammation, apoptosis, and cell proliferation 27 , 28 , 29 , 30 . Nur77 has been extensively studied as an key regulator of immune homeostasis and as an influencer of the balance between cell survival and death in immune cells 31 , 32 and epithelial cells 33 , 34 . The endogenous ligand for Nur77 was unknown until very recently, when it was discovered that Nur77 is a cytosolic sensor for intracellular LPS 35 . This suggests that Nur77 could participate in host responses to intracellular bacterial infections. However, despite a known role as a regulator of inflammation, Nur77 has almost exclusively been examined in the context of noninfectious diseases such as cancer, leaving the importance of Nur77 in bacterial infections largely unknown. Prior to the discovery of Nur77-LPS binding, we reported that Nur77 expression was induced in the bladder in a mouse model of recurrent urinary tract infection (UTI) arising from intracellular reservoirs of uropathogenic Escherichia coli (UPEC) 36 . Furthermore, we observed differences in the rate of rUTI between WT mice and mice with germline deficiency of Nur77 (Nur77-KO). Mice lacking Nur77 maintained higher levels of UPEC intracellular reservoir infection following exfoliation-inducing exposures that successfully reduced UPEC reservoir titers in WT mice 36 . These results suggested that Nur77 expression in the bladder may modulate intracellular UPEC infection.

In this study, we canvassed available single-cell RNAseq datasets to find that Nur77 is ubiquitously expressed in multiple cell types in the bladders of mice and humans. Building on our prior work, we demonstrate in a preclinical mouse model that Nur77 is necessary to limit UPEC bladder infection. Pharmacological treatment with a Nur77 agonist in human urothelial cells in vitro blocked endocytosis and prevented UPEC intracellular invasion and limited UPEC UTI in vivo in the mouse model of UTI. These complimentary findings provide evidence that Nur77 is important for controlling UPEC infection in the bladder and may represent a novel therapeutic target for UTI.

Results from knockout mice implicate Nur77 in persistent UPEC bladder infection

Given the importance of Nur77 in multiple cellular processes that are known to be involved in the UPEC UTI pathogenic cascade, and the results from our prior rUTI model 36 , we reasoned that Nur77 might modulate outcomes of UPEC UTI. Therefore, we performed experimental UPEC UTI in mice with germline deficiency of Nur77 (Nur77-KO). First, we confirmed that there were no baseline differences in gross urothelial morphology that could affect UTI outcomes. There were no apparent alterations in urothelial architecture at baseline in the absence of Nur77. Both WT and Nur77-KO bladders exhibited typical staining of uroplakin 2 (urothelial cells), p63 (intermediate and basal cells), and keratin 5 (basal cells) (Fig.  S1 ). Female 6–7-week-old wild-type (WT) C57BL/6 or Nur77-KO mice were transurethrally inoculated with UPEC strain UTI89 (Fig.  1A ), and UPEC colony-forming units (CFU) were enumerated in urine and bladder tissue. Of note, C57BL/6 mice typically resolve UPEC bacteriuria over the course of one to several weeks; meanwhile, UPEC that have successfully invaded the urothelium and established QIRs are maintained in the bladder for months while urine cultures remain negative 23 , 24 . Here, Nur77-KO mice exhibited approximately tenfold lower UPEC bacteriuria 24 hpi, but this difference had resolved by 1 wpi (Fig.  1B ). There was no difference in the duration of bacteriuria, with most WT and Nur77-KO mice clearing UPEC from urine by 3 wpi (Fig.  1C ). The difference in UPEC bacteriuria 24 hpi was not reflected in bacterial loads in bladder tissue, which were equal 24 hpi and 1 wpi between WT and Nur77-KO mice (Fig.  1D ). In contrast to these results at acute time points, when mice were examined later (3 and 4 wpi; after resolution of UPEC bacteriuria), Nur77-KO mice harbored threefold more UPEC CFU compared to WT controls ( P  < 0.05 at 3 wpi, P  < 0.01 at 4 wpi, Fig.  1E ). These data suggest that Nur77 acts to limit UPEC infection and persistence in bladder tissue.

A Schematic of UPEC UTI mouse model. A Created with BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license. B UPEC titers in urine during the first week of infection; data combined from five independent experiments. Kruskal–Wallis P  < 0.0001; *** P  = 0.0010 by two-tailed Mann–Whitney U test. C UPEC titers in urine collected weekly; data combined from two independent experiments. D Acute infection UPEC titers in bladder tissue homogenates; data from one experiment per time point. E Quiescent reservoir UPEC titers in bladder tissue homogenates; data from two independent experiments per time point. Central tendency lines in graphs ( B , D , E ) denote geometric mean values. Kruskal–Wallis P  < 0.0001; * P  = 0.0119, ** P  = 0.0035, by two-tailed Mann–Whitney U test. Source data are provided as a Source Data file.

Cellular expression of Nur77 in the mouse and human bladder

Nur77 is broadly expressed by multiple cell types. Our previous bulk RNA sequencing (RNAseq) analysis reported Nur77 expression in bladders from naive and UPEC-infected mice and in mice transurethrally inoculated with Gardnerella (Fig.  S2A ). To gain further insight into which cell types in the bladder could be mediating the effect of Nur77 on UPEC titers, we examined published and publicly available single-cell transcriptomics datasets. Tabula Muris contains single-cell (sc)RNAseq transcript analysis of 20 tissues and organs from C57BL/6 mice, including the bladder 37 . Nur77 expression was evident in cells from all Tabula Muris cell clusters in the bladder (Fig.  S2B ). Notably, among the 100,000 cells analyzed from all tissues in Tabula Muris , the “bladder cell” clusters had the second-highest expression of Nur77. Yu et al. performed high-throughput, droplet-based scRNAseq analysis of healthy C57BL/6 mouse and human bladder tissue 38 . Their analysis classified 15 distinct cell clusters in the mouse bladder and 16 clusters in the human bladder. Nur77 expression was detected in leukocytes (monocytes, dendritic cells, T cells, and B cells), all three layers of the urothelium (basal, intermediate, umbrella), and other bladder cell types including fibroblasts, smooth muscle cells, and endothelial cells (Fig.  S2C ).

Absence of Nur77 does not alter bladder immune cell populations before or during UTI

Nur77 is an important regulator of host inflammatory responses 27 , 29 , 32 , 39 . Therefore, we reasoned that the increase in UPEC bladder burden in Nur77-KO mice 3 and 4 wpi could be due to a deficiency in inflammation. First, we established that there were no baseline differences in the populations of myeloid or lymphoid cells in the bladders between naive WT and Nur77-KO mice (Fig.  S3 ). We examined bladders from WT and Nur77-KO mice 24 hpi and 4 wpi by histology and flow cytometry. Both WT and Nur77-KO mice displayed the expected histological inflammation and edema 24 hpi (Fig.  2A ). There were no significant differences in CD45+ cell numbers and only a slight, yet statistically significant, decrease in the proportion of B cells in Nur77-KO mice compared to WT (Fig.  2C ). We also measured UPEC uptake by immune cells as previously described 40 (Fig.  2D ), but there was no difference in the distribution of effector cells that were UPEC+ (Fig.  2E) or in the percentage of each myeloid cell type that were UPEC+ (Fig.  2F ). These data suggest that Nur77 is not necessary for immune cell expansion in the bladder or phagocytosis of UPEC during the acute stages of infection. Following resolution of bacteriuria, histological inflammation and edema resolved in both WT and Nur77-KO mice (Fig.  2G ), and the number of CD45+ cells in the bladder returned to baseline levels 4 wpi (Fig.  2H ). There were no significant differences in the numbers or proportions of CD45+ cell types 4 wpi (Fig.  2I ) when comparing infected WT and Nur77-KO mice. However, differences became apparent when comparing naive to infected mice in each genotype. WT-infected mice had an increase only in the proportion of monocytes 4 wpi (Fig.  2J, K ), whereas Nur77-KO-infected mice had significantly increased PMNs, DCs, monocytes, and macrophages in the bladder 4 wpi (Fig.  2L ). There was a trend toward increased NK cells 4 wpi in WT mice (Fig.  2K ), and this difference was statistically significant in Nur77-KO mice, with a concomitant decrease in T cells (Fig.  2M ). The observation that Nur77-KO mice had proportionally increased myeloid cell populations in the bladder is consistent with the heightened UPEC bacterial burden in Nur77-KO bladders at this time point.

A – F Bladders collected 24 h after mock or UTI89 infection. A H&E staining of formalin-fixed paraffin-embedded bladder tissue sections. Scale bar = 100 μm. B , C Comparison of the numbers ( B ) and relative abundances ( C ) of immune cell types between WT and Nur77-KO mice, combined from two independent experiments. D Gating strategy for UTI89-RFP+ immune cells. E Distribution of UPEC+ myeloid cells in WT and Nur77-KO bladders. F Percentage of each myeloid cell type that was UPEC + . G – M Bladders collected 4 weeks after mock or UTI89 infection. All infected mice in this analysis resolved bacteriuria and harbored quiescent UPEC reservoirs at this time point; data combined from two independent experiments. G H&E staining of formalin-fixed paraffin bladder tissue sections. Scale bar = 100 μm. H , I Comparison of the numbers ( H ) and relative abundances ( I ) of immune cell types between WT and Nur77-KO mice. J – M Comparison of myeloid ( J , L ) and lymphoid ( K , M ) relative abundances between uninfected and infected mice of each genotype. WT ( J , K ); Nur77-KO ( L , M ). All graphs show mean +/− SEM. J * P  = 0.005256; L *** P  = 0.00119, ** P  = 0.000985 (PMNs), P  = 0.000725 (DCs), * P  = 0.010793; (M) ** P  = 0.003107 (T cells), ** P  = 0.002273 (NKs) by two-tailed Mann–Whitney U test with Holm–Šídák method. Source data are provided as a Source Data file.

UPEC IBC formation is increased in the absence of Nur77

The establishment of the persistent reservoirs of UPEC in the bladder that are present 4 wpi requires invasion of urothelial cells during the acute phase of the infection. We hypothesized that the heightened UPEC burden in the bladders of Nur77-KO mice 4 wpi could result from enhanced invasion earlier during infection. To address this hypothesis, we first performed ex vivo gentamicin protection assays that separately enumerated intracellular and extracellular UPEC CFU. Compared to WT mice 3 hpi, Nur77-KO mice had significantly lower UPEC CFU in both the extracellular (washes) and intracellular (gentamicin-protected) bladder compartments. But, by 6 and 24 hpi, there were no differences in either extracellular or intracellular UPEC CFU (Fig.  S4 ). In WT mice, intracellular UPEC at 6 and 24 hpi are known to be primarily present in intracellular bacterial communities (IBCs), that are formed by intracellular replication following invasion, each of which contain ~10 5 bacterial cells. Therefore, CFU data from ex vivo gentamicin protection assays cannot distinguish between UPEC invasion and subsequent intracellular replication. Since each IBC in the bladder represents a successful UPEC invasion event, to examine invasion more specifically, we enumerated IBCs 6 hpi and 24 hpi in LacZ-stained splayed bladders. Despite having lower UPEC CFU 3 hpi in gentamicin protection assays, we observed an approximately twofold increase in the number of IBCs in Nur77-KO mice compared to WT at both 6 and 24 hpi (Figs. 3A and S5 ). Histological and immunofluorescence microscopy analyses confirmed the presence of IBCs in superficial urothelial cells of Nur77-KO mice with similar morphology as seen in WT mice (Fig.  3B ). These results suggest that Nur77 may function in urothelial cells early during infection to limit the UPEC urothelial cell invasion that is needed for IBC formation and eventually leads to persistent intracellular reservoirs.

A Representative images of splayed bladders collected 6 hpi and stained with X-gal to visualize IBCs. IBCs were enumerated in three independent experiments (see Fig.  S5 for data from third experiment). Graphs show the mean +/− SEM. 6 hpi WT and KO n  = 5; 24 hpi WT n  = 7, 24 hpi KO n  = 6. * P  = 0.0438 (6 hpi), * P  = 0.0239 (24 hpi) by two-tailed Mann–Whitney. Source data are provided as a Source Data file. B  Visualization of IBCs (arrows) in bladder sections collected 6 hpi. Left panels stained with H & E. Right panels stained with a primary antibody to keratin 5 (K5, green) and DAPI. UPEC-RFP appear in red. Scale bars = 100 μm.

A small-molecule Nur77 agonist inhibits UPEC intracellular urothelial infection in vitro

The increase in IBC numbers in Nur77-KO mice suggested that Nur77 could function in urothelial cells to limit intracellular UPEC burden. To test this idea, we performed infection experiments in cultured 5637 urothelial cells. First, we performed siRNA knockdown of Nur77 and compared UPEC intracellular infection to control siRNA cells. Despite achieving significant reduction in Nur77 transcript, there were no significant differences in UPEC intracellular infection in these experiments, suggesting that Nur77 is not essential to permit invasion and intracellular infection in vitro (Fig.  S6 ). Next, we used the Nur77 agonist cytosporone B (CsnB) 41 to activate Nur77. CsnB, is an octaketide isolated from an endophytic fungus that was the first identified natural ligand for Nur77 41 . CsnB has a strong affinity for Nur77 and has been widely used to specifically activate Nur77 in vitro 41 , 42 , 43 , 44 , 45 , 46 . Therefore, CsnB would allow us to determine the effect of activating Nur77 at various time points during UPEC infection. Given that Nur77-KO mice had increased UPEC infection, we hypothesized that activation of Nur77 with CsnB would decrease UPEC urothelial infection. Before performing infection experiments, we confirmed that incubation with CsnB did not affect UPEC viability (Fig.  S7A ) or the bacterial hemagglutination (HA) titers that are a measure of UPEC type 1 pili expression (Fig.  S7B ). Next, we performed a series of in vitro UPEC infection experiments in cultured urothelial cells treated with CsnB. In the first experimental setup, CsnB was applied to cultured 5637 urothelial cells at the same time as UPEC inoculation (Fig.  4A , “Concurrent”). Then UPEC initial binding to cells after 30 min, as well as intracellular infection 2, 4, and 6 hpi, were measured. Aligned with the HA titer results demonstrating no effect of CsnB on type 1 pili, CsnB did not affect UPEC binding to urothelial cells (Fig.  S7C ). However, gentamicin protection assays to enumerate intracellular bacteria demonstrated that concurrent CsnB treatment caused a dose-dependent decrease in intracellular UPEC CFU 2, 4 and 6 hpi (Figs.  4B and  S8A ). Next, we determined whether CsnB would still be effective if it were administered to the cells after the 30-minute UPEC invasion phase (Fig.  4C , “Post-invasion”). Under these conditions, the highest dose of CsnB reduced intracellular UPEC (Fig.  4D ). These data demonstrated that CsnB treatment is effective at reducing intracellular UPEC infection burden.

A Schematic of “Concurrent Treatment” experiments in which 5637 cells were treated with DMSO vehicle control (−) or increasing concentrations of CsnB and infected with UPEC at the same time. B Time course of intracellular UPEC titers in the Concurrent Treatment model. # denotes no detectable CFU. Data are from n  = 3 biological replicates per experimental condition (see Fig.  S8A for additional independent experiment). **** P  < 0.0001, *** P  = 0.0004, ** P  = 0.0053 (6 hpi DMSO vs 10 μM), ** P  = 0.0019 (6 hpi 10 vs 100 μM), * P  = 0.0240 by one-way ANOVA with Šídák’s multiple comparisons test. C Schematic of “Post-invasion Treatment” experiments in which 5637 cells were first infected with UPEC for 30 min to allow invasion to occur, and then treated with vehicle or CsnB. D Time course of intracellular UPEC titers in the Post-invasion Treatment model. Each experimental condition includes n  = 6 biological replicates from two independent experiments (each with n  = 3 biological replicates). ** P =  0.0026 (4 hpi DMSO vs 100 μM), ** P  = 0.0017 (4 hpi 10 vs 100 μM), * P  = 0.0232 (2 hpi DMSO vs 100 μM), * P  = 0.0494 (2 hpi 10 vs 100 μM), by one-way ANOVA with Šídák’s multiple comparisons test. Graphs in ( B , D ) show the mean with SEM. A , C Created with BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license. Source data are provided as a Source Data file.

CsnB-limited intracellular UPEC infection is not explained by loss of urothelial cell viability

Next, we interrogated the mechanism by which CsnB inhibited UPEC infection of urothelial cells. Since Nur77 is known to drive apoptosis and pyroptosis 30 , 35 , we hypothesized that the decreased intracellular bacterial burden could be explained by CsnB triggering urothelial death. Therefore, we measured LDH release to assess urothelial cell viability following treatment with CsnB. CsnB treatment alone for 2 or 4 h did not cause a significant decrease in cell viability; by 6 h of treatment there was a slight, yet statistically significant, decrease in viability only with the highest CsnB dose (vehicle, 100% viable vs. 100 μM CsnB, 89% viable) (Fig.  S9A ). These data demonstrated that CsnB does not strongly affect 5637 cell viability at the time points employed in our intracellular infection experiments. Since there is potential for synergistic effects of the inhibitor and UPEC infection, we likewise measured cell viability following UPEC inoculation using our “Concurrent” CsnB treatment experimental timeline. Cell viability was >96% for all conditions and time points tested, including as late as 24 h, while treatment with the apoptosis-inducing agent staurosporine as a positive control significantly reduced cell viability, killing all of the cells (Fig.  S9B, C ). As a final confirmation that induction of programmed cell death was not responsible for the decrease in UPEC intracellular infection, we observed in Fig.  4 , we used the pan-caspase inhibitor Z-VAD-FMK. Co-administration of Z-VAD-FMK significantly inhibited staurosporine-induced urothelial cell death (Fig.  S9C ) but did not affect the ability of CsnB to reduce UPEC CFU (Fig.  S9D ). Taken together, these data demonstrate that loss of urothelial cell viability is not the mechanism by which CsnB inhibits UPEC intracellular infection in vitro.

CsnB triggers expulsion of UPEC from infected urothelial cells

Urothelial cells can rapidly expel internalized UPEC back to the cell exterior via a process involving Rab27b 16 , 18 . We next tested whether enhanced expulsion was a mechanism by which CsnB treatment reduced intracellular UPEC. We measured UPEC expelled from urothelial cells using a previously published assay 17 . As in our initial experiments, UPEC was added to urothelial cells for 30 min, and then were washed to remove non-adherent bacteria. However, for expulsion experiments, instead of adding gentamicin, the post-wash medium contained a bacteriostatic antibiotic and mannoside to prevent replication and reattachment of expelled UPEC. UPEC expulsion was calculated as the percentage of bacteria present in the medium at each time point relative to the number of bacteria that were intracellular at the end of the 30-minute invasion period (“initial load”). UPEC expulsion was significantly enhanced by CsnB, both in our “concurrent” treatment (Fig.  5A, B ) and our “post invasion” treatment models (Fig.  5C, D ). Concurrent treatment with the highest CsnB dose resulted in the expulsion of nearly all the initially invaded UPEC (Fig.  5B ). The effect of CsnB in the post-treatment model was more modest. The highest concentration induced expulsion of 15% of initially invading UPEC from urothelial cells 4 hpi (Fig.  5D ). These data indicate that CsnB can trigger UPEC expulsion.

A Schematic of “Concurrent Treatment” experiments in which 5637 cells were treated with DMSO vehicle control (−) or increasing concentrations of CsnB and infected with UPEC at the same time. UPEC CFU were enumerated in the supernatant and expulsion was calculated relative to the initial load of intracellular UPEC enumerated in parallel wells that were lysed after the 30 min invasion period. B Time course of UPEC expulsion from cells treated concurrently with CsnB. # denotes no detectable CFU. Data are from n  = 3 biological replicates per experimental condition. C Schematic of “Post-invasion Treatment” experiments in which 5637 cells were treated with DMSO vehicle control (−) or increasing concentrations of CsnB only after the 30 min invasion period. D Time course of UPEC expulsion from cells in the post-invasion treatment model. Each experimental condition includes n  = 6 biological replicates from two independent experiments (each with n  = 3 biological replicates). Both graphs denote the mean with SEM. * P  = 0.0146, *** P  = 0.0003, by one-way ANOVA with Šídák’s multiple comparisons test. Source data are provided as a Source Data file. A , C Created with BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.

CsnB limits intracellular UPEC infection of urothelial cells by blocking endocytosis

Post-treatment with the highest dose of CsnB decreased intracellular UPEC infection by more than 50% within 2 hpi (Fig.  5B ), which was earlier than when expulsion was detectable. Therefore, we suspected that expulsion is not the only mechanism by which CsnB limits UPEC intracellular infection. Consistent with this idea, in the expulsion experiments, we made the surprising observation that the “initial load” of UPEC intracellular CFU 30 min after inoculation, which measures UPEC invasion (Fig.  6A ), was significantly lower in CsnB-treated cells compared to controls (Figs.  6B and S8B,C ). Treatment with 10 μM CsnB reduced UPEC invasion by 50% and 100 μM CsnB reduced invasion by >90% relative to vehicle-treated cells vehicle-treated cells (Fig.  6C ).

A Schematic of 5637 cell invasion assay. A Created with BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license. B Intracellular UPEC titers 30 min post infection in cells treated concurrently with DMSO vehicle control (−) or increasing concentrations of CsnB. **** P  < 0.0001, ** P  = 0.0046 (DMSO vs 10 μM), ** P  = 0.0050 (1 vs. 10 μM) by one-way ANOVA with Šídák’s multiple comparisons test. C Relative UPEC invasion 30 min post inoculation, calculated from the CFU data in ( B ). Each experimental condition in ( B , C ) includes six biological replicates from two independent experiments. ** P  = 0.0019 (DMSO vs 100 μM), ** P  = 0.0014 (1 vs 100 μM) by Kruskal–Wallis with Dunn’s multiple comparisons test. D Histograms of a time course of intracellular transferrin detected by flow cytometry. The dotted line denotes gating for defining transferrin + cells in ( E ). E Time course of transferrin positivity expressed as % of live cells in each condition. **** P  < 0.0001, ** P  = 0.0065 by one-way ANOVA with Šídák’s multiple comparisons test. D , E Are representative to two independent experiments, each with three biological replicates for each condition and time point. Bars denote the mean with SEM. Source data are provided as a Source Data file.

The ability of CsnB to block initial UPEC invasion of urothelial cells suggested that it could be inhibiting endocytosis. Previous studies have shown that UPEC invasion can be blocked by a variety of endocytosis inhibitors 47 . To determine whether CsnB interferes with endocytosis, we measured the uptake of fluorescently labeled transferrin (Tf) into urothelial cells in the absence and presence of CsnB. Urothelial cells were incubated with Tf for 5, 10 or 15 min and then acid washed to remove extracellular fluorescent signal. Intracellular Tf was detected by flow cytometry. There was a time-dependent increase in Tf uptake in control urothelial cells treated with DMSO (vehicle) (Fig.  6D ), reaching 80% positivity by 15 min (Fig.  6E ). In contrast, cells treated with CsnB had significantly less Tf uptake at each time point (Fig.  6D ), with <20% positivity at 15 min (Fig.  6E ). Together, results from in vitro experiments suggest a multi-pronged inhibitory effect of CsnB on UPEC intracellular infection, both enhancing expulsion and limiting invasion by blocking endocytosis.

A Nur77 agonist inhibits UPEC IBC formation and persistence in the mouse bladder

Our in vitro results supported the idea that CsnB could be a novel treatment to limit UPEC invasion and intracellular infection in the bladder. Since results from Nur77-KO mice demonstrated that UPEC formed more IBCs in the absence of Nur77, we hypothesized that activation of Nur77 by CsnB would reduce IBC numbers. Therefore, we treated mice intraperitoneally with 5 mg/kg CsnB 46 beginning 1 day prior to UPEC inoculation (Fig.  7A ). Since the effects of CsnB on the bladder have not been previously investigated, we performed H&E staining of bladder sections from mice at relevant time points in our treatment model. Importantly, and consistent with our 5637 cell viability data, histological analysis did not detect bladder tissue damage or urothelial disruption in bladders from mice treated with CsnB (Fig.  S10 ). Consistent with our hypothesis, the number of IBCs in the urothelium 6 hpi was significantly lower in mice that received CsnB (Fig.  7B ). The inhibition of intracellular infection by CsnB at the early 6 h time point translated into reduced infectious burden later. By 1 wpi, all mice treated with CsnB had cleared UPEC bacteriuria (Fig.  7C ) and harbored significantly lower bladder bacterial burdens, with more than a 3-log reduction in UPEC CFU compared with vehicle-treated mice (Fig.  7D ). We noticed that the level of UPEC infection in this experiment was higher than we previously observed at this time point (Fig.  1 ), perhaps caused by the stress induced by additional manipulations needed for multiple i.p. injections. Therefore, we repeated the experiment and reduced the number of injections to ensure the reproducibility of the effect of CsnB. We also included Nur77-KO mice to confirm that the effect of CsnB was specific to Nur77 (Fig.  7E ). CsnB treatment administered from 24 h prior to 24 h post UPEC inoculation caused a threefold reduction in UPEC bladder bacterial loads in WT mice 1 wpi. Importantly, this effect was not seen in Nur77-KO mice, which had no significant titer difference between the CsnB and control-treated groups (Fig.  7F ). These results complement the data from Nur77-KO mice shown in Fig.  4 and further support the conclusion that Nur77 controls the development of UPEC IBCs and persistent bladder infection.

A Schematic of CsnB treatment and UPEC UTI mouse model for data in ( B – D ). B Enumeration of IBCs in splayed bladders 6 hpi. n  = 10 mice per group. Results are combined from two independent experiments. * P  = 0.0198 by Mann–Whitney test. C Time course of UPEC titers in urine. ** P  = 0.0016 by Kruskal–Wallis with Dunn’s multiple comparisons test. D UPEC titers in bladder and kidney tissue homogenates 1 wpi. ** P  = 0.0036 by Mann–Whitney test. C , D DMSO-treated n  = 7, CsnB-treated n  = 8. E Schematic of CsnB treatment and UPEC UTI mouse model in WT (DMSO-treated n  = 10, CsnB-treated n  = 9) and Nur77-KO ( n  = 6 per group) mice for data in ( F ). F UPEC titers in bladder tissue homogenates. Results are combined from two independent experiments. *** P  = 0.0007 by Mann–Whitney test. Graphs in ( C , D , F ) denote geometric mean values. Graph in B shows mean +/− SEM. Source data are provided as a Source Data file. A , E Created with BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.

The results of this study demonstrate that Nur77 is an important mediator of host resistance to UTI. When Nur77 is absent, the bladder is more permissive to intracellular UPEC infection, both acutely and after resolution of bacteriuria. Pharmacological activation of Nur77 in vitro and in vivo yields complimentary results, decreasing UPEC urothelial intracellular infection and enhancing clearance from the bladder. The effectiveness of the Nur77 agonist in vivo identifies Nur77 as a promising therapeutic target for UTI. An increasing number of compounds targeting Nur77 are being developed and beneficial effects are being reported for cancer and inflammatory diseases 39 , 42 , 48 . The agonist used here, CsnB, is an octaketide isolated from an endophytic fungus that was the first identified natural ligand for Nur77 41 . CsnB has a strong affinity for Nur77 and not only stimulates the transactivational activity of Nur77, leading to upregulation of Nur77-responsive genes, including Nur77 itself 41 , but also triggers nuclear export of Nur77. When Nur77 translocates to the mitochondria, it is a potent death promoter. It is likely that the effects of CsnB on UPEC UTI involve multiple of these Nur77 functions. Our in vitro data demonstrated that CsnB treatment inhibits UPEC invasion of urothelial cells. The observation that mice treated with CsnB harbored fewer UPEC IBCs in the bladder suggests that CsnB has a similar effect on UPEC urothelial interactions in vivo. Neither type 1 pili nor UPEC binding were affected by CsnB, suggesting that CsnB treatment either prevented uptake of UPEC or rapidly triggered their expulsion. CsnB did not substantially affect urothelial cell viability in our in vitro assays, suggesting that cell death is not required for the agonist to affect UPEC intracellular infection. Even though histological analysis did not reveal apparent disruption of the urothelium, we acknowledge that we cannot exclude the possibility that CsnB-induced apoptosis is involved in vivo. CsnB has been shown to modulate glucose levels, which are also known to affect UTI outcomes. Intraperitoneal injection with 50 mg/kg CsnB (ten-times higher dose than used in our study) increase blood glucose levels 30 min after i.p. injection, but this effect wanes by 2 h. Mice with high blood glucose (streptozocin-induced diabetic model) have increased susceptibility to UTI, with higher bacterial burdens 6, 24, and 72 hpi. Since mice treated with CsnB had decreased susceptibility to UTI, it is unlikely that glucose levels are playing a substantial role in our model. Future studies examining subcellular localization of Nur77 in urothelial cells over the course of UPEC infection and examining the effects of CsnB on the bladder mucosa will distinguish which functions of Nur77 are important for mediating urothelial resistance to UPEC. The data presented here have identified a new host factor involved in urothelial uptake and expulsion of UPEC. To our knowledge, involvement of Nur77 in vesicle trafficking has not been reported; whether the effect of Nur77 on this process is direct or indirect remains to be determined. The finding that UPEC invasion and expulsion can be manipulated by the timing of CsnB treatment provides a new tool that will be useful in studies of host mechanisms modulating the UPEC intracellular infection cascade. It is possible that Nur77 activation could likewise be important for the intracellular lifestyle of other pathogens.

The data from mouse UTI experiments pointed to Nur77 exerting its effect in the urothelium, rather than via myeloid or lymphoid cells. This conclusion is further corroborated by infection experiments in urothelial cells in vitro. Our observations that WT and Nur77-KO displayed similar immune cell populations in the bladder 24 h after UPEC infection are consistent with the results from an E. coli peritonitis model that displayed no differences in inflammatory cytokine levels, neutrophil or macrophage numbers in WT and Nur77-KO mice in all body compartments tested 49 . In addition, isolated peritoneal macrophages from WT and Nur77-KO mice showed no differences in cytokine expression patterns in response to E. coli 49 . Surprisingly few studies have examined the role of Nur77 in bacterial infections. Nur77 is known to be expressed by gut and lung epithelial cells but has almost exclusively been studied in the context of noninfectious diseases such as cancer. Perhaps the most relevant evidence suggesting that Nur77 could participate in host responses to intracellular bacterial infection comes from the recent discovery that Nur77 is a cytosolic sensor for intracellular LPS 35 . The endogenous ligand(s) for Nur77 were unknown until very recently. Biochemical screening discovered that Nur77 is an LPS-binding protein 35 . Additional data demonstrated that Nur77 binding of LPS and of mitochondrial DNA leads to activation of the non-canonical NLRP3 inflammasome 35 . UPEC are known to activate the NLRP3 inflammasome in urothelial cells in an NFκB-dependent manner 50 . We reason that the LPS-sensing function of Nur77 could be particularly important for host recognition of UPEC urothelial invasion during UTI. Our results suggest that Nur77 should be further examined as a host sensor of bacterial infections, especially those at mucosal surfaces and by pathogens that invade epithelial cells.

This study has some limitations that provide opportunities for future research. First, we used available Nur77-KO mice that lack Nur77 in all cell types. Future generation of urothelial-specific Nur77-KO mice would allow more definitive assessment of the contribution of Nur77 expression in urothelial cells to the host response to UTI in vivo. A second limitation is that our flow cytometry analysis of the mouse bladder examined immune cell populations generally and did not assess activation states or sub-populations. Therefore, it remains possible that certain immune cells contribute to the overall phenotypes observed in Nur77-KO mice. For example, we did not distinguish between resident and circulating monocyte-derived macrophages, which are known to have distinct functions in the bladder response to UTI 51 . We also did not examine innate lymphoid cells (ILCs), which are largely unstudied in the bladder but may serve yet unknown functions in UTI responses. Future studies examining immune populations in Nur77-KO bladders that are known to be important for UTI, such as M1-like and M2-like macrophages 52 and tissue-resident T cells 53 will further illuminate the contribution of Nur77 to immune responses to, and control of, UTI. Although here we examined Nur77 only during the initial UTI, it is likely that Nur77 influences adaptive immune responses in the bladder. Nur77 is a well-established mediator of T-cell function, being first discovered for its role in triggering apoptosis during T-cell-negative selection 54 . It is an early activation marker of TCR signaling 55 , and a Nur77-GFP reporter mouse is frequently used to monitor T-cell activation in vivo 56 . Nur77 is used as a reporter gene for antigen receptor signaling in T cells and iNKT cells 56 , 57 . Nur77 is an important mediator of peripheral B cell tolerance 58 . Studies in mice have demonstrated that adaptive immunity mediated by T and B cells is established in the bladder following clearance of an initial UPEC UTI 40 . Future studies using the well-established UPEC challenge infection model in Nur77-KO mice are needed to uncover additional roles for Nur77 in adaptive immunity to UTI. UTI are highly recurrent, with up to 50% of women experiencing rUTI in their lifetime 1 . A better understanding of what host factors control adaptive immune responses to UTI could reveal new therapeutic avenues to prevent recurrences. Finally, our data demonstrate efficacy of pharmacological targeting of Nur77 given prophylactically to decrease UTI severity by limiting UPEC intracellular invasion and enhancing its clearance from the bladder. Further studies are needed to discover whether a Nur77 agonist would be an effective treatment if given after UTI onset or to eliminate established bladder reservoirs.

In summary, this study establishes that Nur77 is an important host factor controlling intracellular UPEC infection in the bladder. We propose that pharmacological targeting of Nur77 should be explored as a new therapeutic approach for UTI. Since such an approach would target a host protein, it would not engender resistance among uropathogens and should be agnostic to antimicrobial resistance in multiple organisms, especially those which are known to invade urothelial cells, including E. coli , Klebsiella pneumoniae 59 , 60 , Pseudomonas aeruginosa 61 , Acinetobacter baumannii 25 , and Enterococcus faecalis 62 . These uropathogens are responsible for substantial clinical morbidity and mortality in the community and in hospitalized patients, an increasing number of whom have few antibiotic options. Thus, the public health significance of a new efficacious approach targeting a host factor to help eliminate uropathogens from the bladder, as proposed here, is vast.

All animal procedures were performed with the prior approval of the Washington University Institutional Animal Care and Use Committee (protocols #20-0031 and 23-0015).

Study design

The objective of our study was to assess the contribution of the mammalian orphan nuclear receptor Nur77 (aka NR4A1) to urinary tract infection. For this, we assessed outcomes of experimental UTI with UPEC, profiled immune cell populations, and analyzed urothelial architecture in C57BL/6 mice lacking Nur77 (Nur77-KO) compared to wild-type control mice. We examined Nur77 expression in published single-cell RNAseq datasets from bladder cells isolated from both mice and humans. We also used the small-molecule Nur77 agonist cytosporone B (CsnB) with the urothelial 5637 cell line and in vivo infection models to understand the mechanism by which Nur77 interferes with UPEC intracellular infection. Each in vivo experiment was performed at least in duplicate. Intermediary and final endpoints were predetermined for all experiments. Mouse numbers for experiments assessing UTI were predetermined based on our prior published data on UPEC titers in urine and tissue 24 , 63 , 64 . For flow cytometry, a pilot study of mice was conducted to reduce the total number of animals used, estimate variability among animals, and evaluate procedures. Mouse inoculations generally were not blinded, but investigators performing immunofluorescence microscopy and flow cytometry were blinded to the experimental groups during data acquisition. Age-matched WT and Nur77-KO mice were randomly allocated to mock or UTI89 infection and/or to vehicle or CsnB treatment groups. Each in vitro experiment was performed at least in duplicate, with at least six biological replicates per condition and time point. The number of samples and the number of experimental replicates for each experiment are reported in figure captions. All data points are included in each figure.

All mice were group-housed in a temperature-controlled room in a specific pathogen-free facility with a 12 h light/12 h dark cycle. Mice were given water and standard chow diet ad libitum. Nur77-deficient mice were purchased from Jackson Laboratories (B6;129S2-Nr4a1tm1Jmi/J #006187) and bred in house thereafter. Age-matched C57BL/6J mice (Jackson Laboratories; #000664) were purchased and housed in our facility a minimum of seven days prior to experimentation. We compared Nur77-KO mice to wild-type C57BL/6J to be consistent with the majority of published studies that have used the Nur77-KO strain. An alternative option for future studies would be to use the B6129PF2/J that is an F2 hybrid from C57BL/6J females (B6) and 129P3/J males (129P) approximate controls for genetically engineered strains that were generated with 129-derived embryonic stem cells, which is how the Nur77-KO strain was generated. In order to utilize the well-established model of transurethral inoculation, and because UTI predominantly affect women, all experiments only used female mice.

Bacterial strains and preparation

Uropathogenic E. coli strains UTI89, harboring a kanamycin resistance cassette 65 , or UTI89-RFP 40 (kind gift from Molly Ingersoll) were grown aerobically at 37 °C in static liquid culture in Lysogeny Broth (LB) medium, or on LB agar plates with 50 μg/ml kanamycin. Mouse inocula containing 1 × 10 7 CFU UPEC in 50 µL PBS (OD = 0.35) were prepared for each experiment from static LB liquid cultures 63 .

Mouse urinary tract infection and cytosporone B treatment

Experiments were performed essentially as described previously 24 , 63 . Seven-week-old female wild-type C57BL/6 J and Nur77-KO mice were anesthetized and then transurethrally inoculated with 1 × 10 7 CFU UPEC in 50 µL PBS. Urine was collected throughout the course of infection (6 and 24 hpi and weekly thereafter) to measure bacteriuria. In some experiments, either 0.1 mg CsnB (Tocris, Cat. No. 5459) or vehicle (DMSO) in 100 μL PBS (5 mg/kg final dose 46 ;) was given IP (intraperitoneal) at the indicated time points pre- and post infection. Injections were freshly prepared by diluting a CsnB stock solution (reconstituted in DMSO) or an equal volume of DMSO as vehicle control. At the time of sacrifice, bladders and kidneys were harvested, homogenized in 1 mL sterile PBS and plated on LB plates containing 50 µg/mL kanamycin for CFU measurements.

Nur77 expression analysis

We obtained Nur77 expression data from two previously published datasets 37 , 38 . t-distributed stochastic neighbor embedding (t-SNE) plots of annotated bladder cell types, t-SNE plots of Nur77-expressing bladder cells, and violin plots of Nur77 expression levels in C57BL/6 mouse bladders were produced from https://tabula-muris.ds.czbiohub.org . Average Nur77 expression data in bladder cells were obtained from ref. 38 and are available in our Source Data file. Data were plotted in GraphPad Prism.

Flow cytometry

Bladders were aseptically harvested postmortem and digested enzymatically as described in ref. 63 for the 24 hpi (Fig.  3B–F ) experiments. In short, the bladders were emptied, placed in 100 μL Tyrode’s solution (140 mM NaCl, 5 mM KCl, 1 mM MgCl 2 , 10 mM d -Glucose and 10 mM HEPES, pH 7.4), and minced with scissors. Digestion buffer containing 5 mg BSA, 0.03 mM CaCl 2 , 132.5 units Collagenase Type 1, 96.4 units Collagenase Type III, 50 units Collagenase Type VI, 10 units DNase, 115 units Papain, 50 units Pan Collagenase and 10.5 units Hyaluronidase in Tyrode’s Solution was added to the bladders and incubated at 37 °C on a nutator for 40 min. The samples were then pipetted up and down for one minute to further separate the bladder cells and centrifuged for 10 min at 350× g at 4 °C. The supernatant was discarded, the pellet was resuspended in 1 mL Accutase (Millipore Sigma; #A6964) to detach any cells adhering to the tube and nutated for 10 min at 37 °C. For naive (Fig. S2) and 4 wpi (Fig.  3H–M ) experiments, aseptically harvested bladders were digested with liberase and DNase as described in ref. 43 . Following digestion, the samples were centrifuged and the pellets were resuspended in RBC cell lysis buffer (155 mM NH 4 Cl, 10 mM KHCO 3 ) for 1 min at room temperature and 9 mL of PBS was added to stop lysis. The samples were then filtered through a 70-μm filter and any remaining tissue was manually disrupted to make a final cell suspension. The samples were then centrifuged and the pellets were resuspended in 1 mL PBS. Cell counts were obtained via hemacytometer and 1 × 10 6 cells were added to FACS tubes. The dead cells were stained with Live/Dead Fixable Blue (Thermo Fisher Scientific; #L34962) and incubated on ice for 30 min in the dark. The cell suspension was washed with FACS buffer (PBS containing 0.01% FBS and 0.1% sodium azide), centrifuged, and incubated with Fc Block (BD Pharmingen; #553142) for 15 min at 4 °C. Following the incubation, antibody cocktail containing CD45 BV510 (BD Horizon; #563891), MHCII BUV395 (BD Optibuild; #743876), CD19 BUV661 (BD Horizon; #612971), CD3 BUV805 (BD OptiBuild; #741982), CD11b BV570 (Biolegend; #101233), F4/80 BB700 (BD Optibuild; #746070), NK1/1 PE/Dazzle594 (Biolegend; #108748), Ly6C PE-Cy5.5 (Novus Biologicals; #N100-65413PECY55 ) , Ly6G AF700 (Biolegend; #127621), Siglec F AF421 (Biolegend; #155509) was added and incubated for 45 min at 4 °C covered from light. After antibody staining, the cells were washed two times in FACs buffer, filtered through 40-µM nylon mesh and analyzed on the Cytek® Aurora (Cytek Biosciences) flow cytometer. Blood was collected via cardiac puncture at the time of sacrifice and joined the bladders at the red blood cell lysis step. All flow analysis was performed using FlowJo (Treestar) software. Immune cell populations were identified according to our established gating strategy (Fig.  S11 ).

Bladders were harvested at the specified time points and fixed in 10% formalin at room temperature overnight and then transferred to 70% ethanol. Bladders were embedded in paraffin and sagittal sections were prepared and mounted on glass slides. For histological analysis, slides were stained with hematoxylin and eosin (H&E) according to standard protocol. For immunofluorescence microscopy, slides were placed onto glass holding trays and then placed into fresh Histo-Clear® Histological Clearing Agent, (National Diagnostics), for 10 min two times. The trays were drained, then moved to 100% ethyl alcohol for 10 min two times, and then to 95% ethyl alcohol two times for 10 min. Finally, glass trays holding the slides were placed under running water for 10 min. During the deparaffinization, fresh pH 9 and 6 buffered antigen retrieval solutions were made and brought to a boil in 50 ml BD conical tubes in a glass beaker filled with water in a steamer. After washing, glass slides were placed into the appropriate buffer around 90–100 °C without allowing the slides to dry out and boiled for 15 and 30 min in for pH 9 and pH 6 buffer, respectively. Slides were then cooled and allowed to cool to 60 °C and were washed in 0.5% Triton X-100 in phosphate-buffered saline (PBS) at room temperature. Glass slides were removed from wash buffer one at a time, placed horizontally into humidified slide boxes, and the hydrophobic boundary was marked around the perimeter with a PAP PEN. About 300 μl of 10% heat-inactivated horse serum (HIHS) and 3% bovine serum albumin (BSA) in 0.5% Triton X-100 PBS were placed onto each slide for blocking. Slides were incubated in a closed, humidified slide box for 1 to 2 h. During blocking, primary antibody cocktails of chicken anti-KRT 5 1:500; goat anti-P63 1:300; mouse anti-Upk2 1:50 were prepared in 1% HIHS and 1% BSA in 0.5% Triton X-100 sufficient for around 300 μl per slide. The blocking solution was removed by vacuum and the primary antibody added onto the slide without disturbing hydrophobic perimeter. Slides were incubated overnight in humidified slide boxes at 4 °C. The following day, the primary antibody was removed, and the slides were washed for 10 min in fresh 0.5% Triton X-100 PBS twice. During washes, secondary antibody cocktails were prepared in 1% HIHS and 1% BSA in 0.5% Triton X-100 for 300 μl per slide, and two drops of NucBlue® nuclear-staining reagent (DAPI) were added per milliliter of antibody cocktail. Slides were removed from the washing buffer and hydrophobic perimeters redrawn then a secondary antibody cocktail was added. Slides were incubated in the dark at room temperature for 30 min to 1 h. After incubation, slides were washed, and previously warmed DAKO glycerol mounting medium was applied before the coverslip. Slides were stored overnight at 4 °C in slide folders.

IBC enumeration

Bladders were harvested 6 h and 24 h post infection and splayed out with pins on Slygard® 184 (The Dow Chemical Company; #2646340) cured six-well plates containing PBS. The bladders were then washed with PBS and fixed in 4% paraformaldehyde for 1 h at room temperature. Following fixation, the paraformaldehyde was removed, and the bladders were washed one time with PBS and three times (5 min each) in LacZ wash solution (2 mM MgCl 2 , 0.01% w/v sodium deoxycholate and 0.02% w/v NP40). After the washes, LacZ stain (LacZ wash solution plus 1 mg/ml X-gal and 1 mg/mL K-ferrOcyanide/K-ferrIcyanide) was applied to each bladder and incubated at 30 °C overnight protected from light. IBCs were quantified by counting the number of X-gal stained (blue) bacterial clusters, which appear as distinct round puncta under a dissecting microscope.

Bladder epithelial cell infections and CsnB treatments

The human bladder epithelial cells, 5637 (ATCC; #HTB-9), were grown in RPMI-1640 medium supplemented with 10% FBS. At confluency, the cells were plated in a 24-well plate at a density of 2 × 10 5 cells/well and incubated overnight at 37 °C, 5% CO 2 . The following day, the cells were infected with UPEC at a MOI of 100, centrifuged (spinoculated) and incubated for 30 min at 37 °C, 5% CO 2 . Cells were washed three times with PBS to remove non-adherent bacteria. Bacterial binding was assessed by lysing designated wells with 0.1% Triton X-100 in PBS for 10 min at RT on orbital rocker, serially diluting, and plating on LB+kan plates. The remaining wells were treated with 100 μg/mL gentamicin. Bacterial “initial load” was assessed by lysing infected cells in designated wells after 10 min of gentamicin treatment and three additional PBS washes. Relative invasion was calculated by dividing the intracellular CFU in each well by the average input CFU in the DMSO control wells. At this time, to the expulsion wells were added 100 mM methyl α-mannopyranoside and 25 ug/ml trimethoprim for the remaining specified times. At 2, 4, or 6 h post UPEC exposure, one-tenth of each supernatant was removed and plated on LB+kan plates. The intracellular infection wells were incubated in the presence of gentamicin for the indicated lengths of time and then lysed for CFU enumeration. Cytosporone B (Sigma; #C2997) was given at indicated doses (vehicle only, 1 μM, 10 μM, or 100 μM; concentration range based on previous reports 66 , 67 ) either at the time of UPEC infection (concurrent treatment) or after the gentamicin treatment and washing to remove extracellular bacteria (post-invasion treatment) and continuously throughout the assay. Each condition employed three wells in each assay.

Targeted knockdown of Nur77 with siRNA transfection

In all, 5637 cells were seeded in 24-well plates at density of 1 × 10 5 in RPMI media (supplemented with 10% FBS and 1% penicillin–streptomycin) and maintained at 37 °C in a humidified atmosphere containing 5% CO 2 . On the day of transfection, twenty picomoles of Nur77-targeting siRNA (SR302153B, ORIGENE, Rockville, MD) and non-targeting siRNA (SR30004, ORIGENE, Rockville, MD) were each combined with diluted Lipofectamine RNAiMAX transfection reagent (Invitrogen Inc., Carlsbad, CA) in 1×Opti-MEM reduced serum medium (without antibiotics). Each mixture was incubated at room temperature for 10 min to allow the formation of siRNA-lipid complexes. The siRNA-lipid complexes were added to each well containing 5637 cells at 60–70% confluency, gently rocked to ensure even distribution and incubate for 24 h at 37 °C in a humidified atmosphere containing 5% CO 2 . After incubation, the transfection media was replaced with 1 mL of fresh complete RPMI media, and cells were further incubated for 24 h and then UPEC infection experiments were performed as described above. The efficiency of siRNA-mediated knockdown was assessed by real-time PCR (qRT-PCR) to quantify target mRNA. Primers Nur77 F 5’-GGA CAA CGC TTC ATG CCA GCA T-3,’ Nur77 R 5’-CCT TGT TAG CCA GGC AGA TGT AC-3’, GAPDH F 5’-CAT CAC TGA CAC CCA GAA GAC TG-3, GAPDH’R 5’-ATG CCA GTG AGC TTC CCG TTC AG-3’. Cycling parameters: 95 °C for 3 min initial denaturation, Denaturation at 95 °C for 10 s Extension at 60 °C for 30 s X40 cycles. qPCR machine Applied Biosystem 7500 Fast Real-Time PCR System.

Cytotoxicity assays

At the specified time points, cytotoxicity of CsnB-treated 5637 bladder cells was determined by measuring LDH release with the CytoTox 96® Non-Radioactive Cytotoxicity Assay (Promega; #PRG1780) per manufacturer instructions. Absorbance values were recorded on a Tecan Infinite M-Plex plate reader (Männedorf, Switzerland). In each experiment, parallel vehicle-treated cells were lysed with 0.1% Triton to measure maximal LDH release. The % killed cells was calculated relative to the maximum LDH release (absorbance/triton max absorbance)*100, which was then used to calculate % viability (100 − % killed).

Cytotoxicity of cytosporone B to UPEC

Cytotoxicity of CsnB against UPEC was evaluated under conditions that mimicked those of the urothelial infection experiments. UPEC were grown in LB and an inoculum was prepared in PBS. UTI89 and the indicated doses of CsnB, or DMSO vehicle control, were added to RPMI + FBS media in a 24-well plate and incubated 30 min at 37 °C with 5% CO 2. Following incubation, samples were serially diluted and plated to enumerate UPEC cfu.

Hemmaglutination assay

Type 1 piliation of UPEC was assessed with the standard hemagglutination assay. UTI89 from a 2 × 24 h LB static liquid culture was diluted to OD 600  = 1 and 1 mL was spun and the pellet resuspended in 100 μL PBS. The bacterial suspension was mixed initially 1:1 (25 μL each), and subsequently seven additional serial 2-fold dilutions were prepared with PBS with or without 100 μM CsnB in a V-bottom 96-well plate. Fresh guinea pig red blood cells prepared in PBS were added to each well on the plate and incubated for 2 h at 4 °C. HA titers were determined as the maximal bacterial dilution that produced a “haze” in the well.

Statistical analysis

For mouse and in vitro studies, statistical and graphical analyses were performed using GraphPad Prism software (version 10.0 or earlier). Normality was determined by the Shapiro–Wilk normality test. For comparisons between two groups, unpaired t tests were used on normally distributed samples and Mann–Whitney tests on samples that did not pass the normality test. One-way analysis of variance (ANOVA) was used on normally distributed samples, and Kruskal–Wallis tests were performed on samples that did not pass the normality test. Šídák’s multiple comparisons test was performed for CsnB dose-response experiments. All measurements were taken from distinct samples. In Figs.  1 B, C and 7C , the measurements correspond to distinct samples taken sequentially over different time points from the same animals as indicated. Results were considered statistically significant at P  < 0.05 and exact P values are indicated in the figure legends. For bacterial CFU data, bars represent the geometric mean. For flow cytometry data and IBC enumeration, error bars represent mean and SEM unless otherwise noted. For all figures with multiple comparisons across groups, except those involving repeated measurements over time, all significant differences across groups are noted, and a lack of notation indicates a lack of statistical significance. For figures with multiple comparisons over time, only significant differences relative to controls at the same time point are shown, and lack of notation indicates a lack of significance relative to those controls. Other relevant comparisons across groups were noted as indicated in each figure legend.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Single-cell RNAseq data shown in Fig.  S2 are available in the previously published studies https://tabula-muris.ds.czbiohub.org and ref. 38 . All data generated in this study are provided in the Source Data file.  Source data are provided with this paper.

Foxman, B. Urinary tract infection syndromes: occurrence, recurrence, bacteriology, risk factors, and disease burden. Infect. Dis. Clin. North Am. 28 , 1–13 (2014).

Article   PubMed   Google Scholar  

Foxman, B. Epidemiology of urinary tract infections: incidence, morbidity, and economic costs. Am. J. Med. 113 , 5S–13S (2002).

Foxman, B. et al. Risk factors for second urinary tract infection among college women. Am. J. Epidemiol. 151 , 1194–1205 (2000).

Article   CAS   PubMed   Google Scholar  

Craig, J. C. et al. Prevention of recurrent urinary tract infection in children with vesicoureteric reflux and normal renal tracts (PRIVENT) investigators, antibiotic prophylaxis and recurrent urinary tract infection in children. New Engl. J. Med. 361 , 1748–1759 (2009).

Anger, J. et al. Recurrent uncomplicated urinary tract infections in women: AUA/CUA/SUFU guideline. J. Urol. 202 , 282–289 (2019).

Beerepoot, M. A. J. et al. Lactobacilli vs antibiotics to prevent urinary tract infections: a randomized, double-blind, noninferiority trial in postmenopausal women. Arch. Intern. Med. 172 , 704–712 (2012).

Selekman, R. E. et al. Uropathogen resistance and antibiotic prophylaxis: a meta-analysis. Pediatrics 142 , e20180119 (2018).

Sharma, K. et al. Early invasion of the bladder wall by solitary bacteria protects UPEC from antibiotics and neutrophil swarms in an organoid model. Cell Rep. 36 , 109351 (2021).

Liu, S.-C., Han, X.-M., Shi, M. & Pang, Z.-L. Persistence of uropathogenic Escherichia coli in the bladders of female patients with sterile urine after antibiotic therapies. J. Huazhong Univ. Sci. Technol. Med. Sci. 36 , 710–715 (2016).

Article   Google Scholar  

Blango, M. G. & Mulvey, M. A. Persistence of uropathogenic Escherichia coli in the face of multiple antibiotics. Antimicrob. Agents Chemother. 54 , 1855–1863 (2010).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Wu, Z. et al. Pyroptosis engagement and bladder urothelial cell-derived exosomes recruit mast cells and induce barrier dysfunction of bladder urothelium after uropathogenic E. coli infection. Am. J. Physiol. Cell Physiol. 317 , C544–C555 (2019).

Thumbikat, P., Berry, R. E., Schaeffer, A. J. & Klumpp, D. J. Differentiation-induced uroplakin III expression promotes urothelial cell death in response to uropathogenic E. coli . Microbes Infect. 11 , 57–65 (2009).

Klumpp, D. J. et al. Uropathogenic Escherichia coli potentiates type 1 pilus-induced apoptosis by suppressing NF-kappaB. Infect. Immun. 69 , 6689–6695 (2001).

Nagamatsu, K. et al. Dysregulation of Escherichia coli α-hemolysin expression alters the course of acute and persistent urinary tract infection. Proc. Natl. Acad. Sci. USA 112 , E871–E880 (2015).

Wang, C. et al. Atg16L1 deficiency confers protection from uropathogenic Escherichia coli infection in vivo. Proc. Natl. Acad. Sci. USA 109 , 11008–11013 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Song, J. et al. TLR4-mediated expulsion of bacteria from infected bladder epithelial cells. Proc. Natl. Acad. Sci. USA 106 , 14966–14971 (2009).

Miao, Y., Li, G., Zhang, X., Xu, H. & Abraham, S. N. A TRP channel senses lysosome neutralization by pathogens to trigger their expulsion. Cell 161 , 1306–1319 (2015).

Miao, Y. et al. Collaboration between distinct Rab small GTPase trafficking circuits mediates bacterial clearance from the bladder epithelium. Cell Host Microbe 22 , 330–342.e4 (2017).

Mulvey, M. A. et al. Induction and evasion of host defenses by type 1-piliated uropathogenic Escherichia coli . Science 282 , 1494–1497 (1998).

Sharma, K. et al. Dynamic persistence of UPEC intracellular bacterial communities in a human bladder-chip model of urinary tract infection. eLife 10 , e66481 (2021).

Rosen, D. A., Hooton, T. M., Stamm, W. E., Humphrey, P. A. & Hultgren, S. J. Detection of intracellular bacterial communities in human urinary tract infection. PLoS Med. 4 , e329 (2007).

Article   PubMed   PubMed Central   Google Scholar  

Robino, L. et al. Detection of intracellular bacterial communities in a child with Escherichia coli recurrent urinary tract infections. Pathog. Dis. 68 , 78–81 (2013).

Mysorekar, I. U. & Hultgren, S. J. Mechanisms of uropathogenic Escherichia coli persistence and eradication from the urinary tract. Proc. Natl. Acad. Sci. USA 103 , 14170–14175 (2006).

Gilbert, N. M., O’Brien, V. P. & Lewis, A. L. Transient microbiota exposures activate dormant Escherichia coli infection in the bladder and drive severe outcomes of recurrent disease. PLoS Pathog. 13 , e1006238 (2017).

Hazen, J. E., Di Venanzio, G., Hultgren, S. J. & Feldman, M. F. Catheterization of mice triggers resurgent urinary tract infection seeded by a bladder reservoir of Acinetobacter baumannii . Sci. Transl. Med. 15 , eabn8134 (2023).

Elliott, T. S., Reed, L., Slack, R. C. & Bishop, M. C. Bacteriology and ultrastructure of the bladder in patients with urinary tract infections. J. Infect. 11 , 191–199 (1985).

Hsu, H.-C., Zhou, T. & Mountz, J. D. Nur77 family of nuclear hormone receptors. Curr. Drug Targets Inflamm. Allergy 3 , 413–423 (2004).

Herring, J. A., Elison, W. S. & Tessem, J. S. Function of Nr4a orphan nuclear receptors in proliferation, apoptosis and fuel utilization across tissues. Cells 8 , 1373 (2019).

Lith, S. C. & de Vries, C. J. M. Nuclear receptor Nur77: its role in chronic inflammatory diseases. Essays Biochem. 65 , 927–939 (2021).

Li, H. et al. Cytochrome c release and apoptosis induced by mitochondrial targeting of nuclear orphan receptor TR3. Science 289 , 1159–1164 (2000).

Article   ADS   CAS   PubMed   Google Scholar  

Kim, S. O., Ono, K., Tobias, P. S. & Han, J. Orphan nuclear receptor Nur77 is involved in caspase-independent macrophage cell death. J. Exp. Med. 197 , 1441–1452 (2003).

Hiwa, R., Brooks, J. F., Mueller, J. L., Nielsen, H. V. & Zikherman, J. NR4A nuclear receptors in T and B lymphocytes: gatekeepers of immune tolerance. Immunol. Rev. 307 , 116–133 (2022).

Shi, Z. et al. Hypoxia-induced Nur77 activates PI3K/Akt signaling via suppression of Dicer/let-7i-5p to induce epithelial-to-mesenchymal transition. Theranostics 11 , 3376–3391 (2021).

To, S. K. Y., Zeng, W.-J., Zeng, J.-Z. & Wong, A. S. T. Hypoxia triggers a Nur77–β-catenin feed-forward loop to promote the invasive growth of colon cancer cells. Br. J. Cancer 110 , 935–945 (2014).

Zhu, F. et al. The orphan receptor Nur77 binds cytoplasmic LPS to activate the non-canonical NLRP3 inflammasome. Immunity 56 , 753–767.e8 (2023).

O’Brien, V. P., Lewis, A. L. & Gilbert, N. M. Bladder exposure to Gardnerella activates host pathways necessary for Escherichia coli recurrent UTI. Front Cell Infect. Microbiol 11 , 788229 (2021).

Tabula Muris Consortium, Overall coordination, Logistical coordination, Organ collection and processing, Library preparation and sequencing, Computational data analysis, Cell type annotation, Writing group, Supplemental text writing group, Principal investigators. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562 , 367–372 (2018).

Article   ADS   Google Scholar  

Yu, Z. et al. Single-cell transcriptomic map of the human and mouse bladders. J. Am. Soc. Nephrol. 30 , 2159–2176 (2019).

Banno, A., Lakshmi, S. P., Reddy, A. T., Kim, S. C. & Reddy, R. C. Key functions and therapeutic prospects of Nur77 in inflammation related lung diseases. Am. J. Pathol. 189 , 482–491 (2019).

Mora-Bau, G. et al. Macrophages subvert adaptive immunity to urinary tract infection. PLoS Pathog. 11 , e1005044 (2015).

Zhan, Y. et al. Cytosporone B is an agonist for nuclear orphan receptor Nur77. Nat. Chem. Biol. 4 , 548–556 (2008).

Wu, L. & Chen, L. Characteristics of Nur77 and its ligands as potential anticancer compounds (Review). Mol. Med Rep. 18 , 4793–4801 (2018).

CAS   PubMed   PubMed Central   Google Scholar  

Ding, X. et al. Cytosporone B (Csn-B), an NR4A1 agonist, attenuates acute cardiac allograft rejection by inducing differential apoptosis of CD4+T cells. Int. Immunopharmacol. 104 , 108521 (2022).

Guan, Y.-F. et al. Nur77-activated lncRNA WFDC21P attenuates hepatocarcinogenesis via modulating glycolysis. Oncogene 39 , 2408–2423 (2020).

Patiño-Martínez, E. et al. The Nurr7 agonist Cytosporone B differentially regulates inflammatory responses in human polarized macrophages. Immunobiology 227 , 152299 (2022).

Egarnes, B., Blanchet, M.-R. & Gosselin, J. Treatment with the NR4A1 agonist cytosporone B controls influenza virus infection and improves pulmonary function in infected mice. PLoS ONE 12 , e0186639 (2017).

Eto, D. S., Gordon, H. B., Dhakal, B. K., Jones, T. A. & Mulvey, M. A. Clathrin, AP-2, and the NPXY-binding subset of alternate endocytic adaptors facilitate FimH-mediated bacterial invasion of host cells. Cell Microbiol. 10 , 2553–2567 (2008).

Liu, L. et al. Progress and promise of Nur77-based therapeutics for central nervous system disorders. Curr. Neuropharmacol. 19 , 486–497 (2021).

Hamers, A. A. J. et al. Limited role of nuclear receptor Nur77 in Escherichia coli -induced peritonitis. Infect. Immun. 82 , 253–264 (2014).

Demirel, I. et al. Activation of the NLRP3 inflammasome pathway by uropathogenic Escherichia coli is virulence factor-dependent and influences colonization of bladder epithelial cells. Front. Cell Infect. Microbiol. 8 , 81 (2018).

Lacerda Mariano, L. & Ingersoll, M. A. The immune response to infection in the bladder. Nat. Rev. Urol. 17 , 439–458 (2020).

Lacerda Mariano, L. et al. Functionally distinct resident macrophage subsets differentially shape responses to infection in the bladder. Sci. Adv. 6 , eabc5739 (2020).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Rousseau, M., Lacerda Mariano, L., Canton, T. & Ingersoll, M. A. Tissue-resident memory T cells mediate mucosal immunity to recurrent urinary tract infection. Sci. Immunol. 8 , eabn4332 (2023).

Woronicz, J. D., Calnan, B., Ngo, V. & Winoto, A. Requirement for the orphan steroid receptor Nur77 in apoptosis of T-cell hybridomas. Nature 367 , 277–281 (1994).

Lory, N. C. et al. Required for T-cell activation and differentiation. Front. Immunol. 12 , 778916 (2022).

Holzapfel, K. L., Tyznik, A. J., Kronenberg, M. & Hogquist, K. A. Antigen-dependent versus -independent activation of invariant NKT cells during infection. J. Immunol. 192 , 5490–5498 (2014).

Ashouri, J. F. & Weiss, A. Endogenous Nur77 is a specific indicator of antigen receptor signaling in human T and B cells. J. Immunol. 198 , 657–668 (2017).

Tan, C. et al. Nur77 links chronic antigen stimulation to B cell tolerance by restricting the survival of self-reactive B cells in the periphery. J. Immunol. 202 , 2907–2923 (2019).

Rosen, D. A. et al. Utilization of an intracellular bacterial community pathway in Klebsiella pneumoniae urinary tract infection and the effects of FimK on type 1 pilus expression. Infect. Immun. 76 , 3337–3345 (2008).

Oelschlaeger, T. A. & Tall, B. D. Invasion of cultured human epithelial cells by Klebsiella pneumoniae isolated from the urinary tract. Infect. Immun. 65 , 2950–2958 (1997).

Newman, J., Floyd, R. & Fothergill, J. Invasion and diversity in Pseudomonas aeruginosa urinary tract infections. J. Med. Microbiol. 71 , 001458 (2022).

Horsley, H. et al. Enterococcus faecalis subverts and invades the host urothelium in patients with chronic urinary tract infection. PLoS ONE 8 , e83637 (2013).

O’Brien, V. P., Joens, M. S., Lewis, A. L. & Gilbert, N. M. Recurrent Escherichia coli urinary tract infection triggered by Gardnerella vaginalis bladder exposure in mice. J. Vis. Exp. https://doi.org/10.3791/61967 (2020).

Gilbert, N. M. et al. Gardnerella exposures alter bladder gene expression and augment uropathogenic Escherichia coli urinary tract infection in mice. Front. Cell Infect. Microbiol. 12 , 909799 (2022).

Wright, K. J., Seed, P. C. & Hultgren, S. J. Uropathogenic Escherichia coli flagella aid in efficient urinary tract colonization. Infect. Immun. 73 , 7657–7668 (2005).

Li, Q. et al. Loss of lipocalin 10 exacerbates diabetes-induced cardiomyopathy via disruption of Nr4a1-mediated anti-inflammatory response in macrophages. Front. Immunol. 13 , 930397 (2022).

Szczepanski, H. E. et al. NR4A1 modulates intestinal smooth muscle cell phenotype and dampens inflammation-associated intestinal remodeling. FASEB J. 36 , e22609 (2022).

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Acknowledgements

This work was supported by funding from the NIH National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK) R03DK132442 (N.M.G.), K01 DK110225 (N.M.G.), U54 DK104309 (C.M.M.), and R01DK137964 (N.M.G.). The authors thank Molly Ingersoll and Brian Becknell for providing us with the UTI89-RFP strain, Hunter Kuhn and Rebekah Smither for technical assistance with LDH and HA assays, Teri Hreha for helpful advice on flow cytometry analysis, and David Hunstad for insightful discussions and thoughtful comments and edits to our manuscript.

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Christina A. Collins, Lokesh Kumar & Nicole M. Gilbert

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Collins, C.A., Waller, C., Batourina, E. et al. Nur77 protects the bladder urothelium from intracellular bacterial infection. Nat Commun 15 , 8308 (2024). https://doi.org/10.1038/s41467-024-52454-8

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Ethical Issues in Research: Perceptions of Researchers, Research Ethics Board Members and Research Ethics Experts

Marie-josée drolet.

1 Department of Occupational Therapy (OT), Université du Québec à Trois-Rivières (UQTR), Trois-Rivières (Québec), Canada

Eugénie Rose-Derouin

2 Bachelor OT program, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières (Québec), Canada

Julie-Claude Leblanc

Mélanie ruest, bryn williams-jones.

3 Department of Social and Preventive Medicine, School of Public Health, Université de Montréal, Montréal (Québec), Canada

In the context of academic research, a diversity of ethical issues, conditioned by the different roles of members within these institutions, arise. Previous studies on this topic addressed mainly the perceptions of researchers. However, to our knowledge, no studies have explored the transversal ethical issues from a wider spectrum, including other members of academic institutions as the research ethics board (REB) members, and the research ethics experts. The present study used a descriptive phenomenological approach to document the ethical issues experienced by a heterogeneous group of Canadian researchers, REB members, and research ethics experts. Data collection involved socio-demographic questionnaires and individual semi-structured interviews. Following the triangulation of different perspectives (researchers, REB members and ethics experts), emerging ethical issues were synthesized in ten units of meaning: (1) research integrity, (2) conflicts of interest, (3) respect for research participants, (4) lack of supervision and power imbalances, (5) individualism and performance, (6) inadequate ethical guidance, (7) social injustices, (8) distributive injustices, (9) epistemic injustices, and (10) ethical distress. This study highlighted several problematic elements that can support the identification of future solutions to resolve transversal ethical issues in research that affect the heterogeneous members of the academic community.

Introduction

Research includes a set of activities in which researchers use various structured methods to contribute to the development of knowledge, whether this knowledge is theoretical, fundamental, or applied (Drolet & Ruest, accepted ). University research is carried out in a highly competitive environment that is characterized by ever-increasing demands (i.e., on time, productivity), insufficient access to research funds, and within a market economy that values productivity and speed often to the detriment of quality or rigour – this research context creates a perfect recipe for breaches in research ethics, like research misbehaviour or misconduct (i.e., conduct that is ethically questionable or unacceptable because it contravenes the accepted norms of responsible conduct of research or compromises the respect of core ethical values that are widely held by the research community) (Drolet & Girard, 2020 ; Sieber, 2004 ). Problematic ethics and integrity issues – e.g., conflicts of interest, falsification of data, non-respect of participants’ rights, and plagiarism, to name but a few – have the potential to both undermine the credibility of research and lead to negative consequences for many stakeholders, including researchers, research assistants and personnel, research participants, academic institutions, and society as a whole (Drolet & Girard, 2020 ). It is thus evident that the academic community should be able to identify these different ethical issues in order to evaluate the nature of the risks that they pose (and for whom), and then work towards their prevention or management (i.e., education, enhanced policies and procedures, risk mitigation strategies).

In this article, we define an “ethical issue” as any situation that may compromise, in whole or in part, the respect of at least one moral value (Swisher et al., 2005 ) that is considered socially legitimate and should thus be respected. In general, ethical issues occur at three key moments or stages of the research process: (1) research design (i.e., conception, project planning), (2) research conduct (i.e., data collection, data analysis) and (3) knowledge translation or communication (e.g., publications of results, conferences, press releases) (Drolet & Ruest, accepted ). According to Sieber ( 2004 ), ethical issues in research can be classified into five categories, related to: (a) communication with participants and the community, (b) acquisition and use of research data, (c) external influence on research, (d) risks and benefits of the research, and (e) selection and use of research theories and methods. Many of these issues are related to breaches of research ethics norms, misbehaviour or research misconduct. Bruhn et al., ( 2002 ) developed a typology of misbehaviour and misconduct in academia that can be used to judge the seriousness of different cases. This typology takes into consideration two axes of reflection: (a) the origin of the situation (i.e., is it the researcher’s own fault or due to the organizational context?), and (b) the scope and severity (i.e., is this the first instance or a recurrent behaviour? What is the nature of the situation? What are the consequences, for whom, for how many people, and for which organizations?).

A previous detailed review of the international literature on ethical issues in research revealed several interesting findings (Beauchemin et al., 2021 ). Indeed, the current literature is dominated by descriptive ethics, i.e., the sharing by researchers from various disciplines of the ethical issues they have personally experienced. While such anecdotal documentation is relevant, it is insufficient because it does not provide a global view of the situation. Among the reviewed literature, empirical studies were in the minority (Table  1 ) – only about one fifth of the sample (n = 19) presented empirical research findings on ethical issues in research. The first of these studies was conducted almost 50 years ago (Hunt et al., 1984 ), with the remainder conducted in the 1990s. Eight studies were conducted in the United States (n = 8), five in Canada (n = 5), three in England (n = 3), two in Sweden (n = 2) and one in Ghana (n = 1).

Summary of Empirical Studies on Ethical Issues in Research by the year of publication

Further, the majority of studies in our sample (n = 12) collected the perceptions of a homogeneous group of participants, usually researchers (n = 14) and sometimes health professionals (n = 6). A minority of studies (n = 7) triangulated the perceptions of diverse research stakeholders (i.e., researchers and research participants, or students). To our knowledge, only one study has examined perceptions of ethical issues in research by research ethics board members (REB; Institutional Review Boards [IRB] in the USA), and none to date have documented the perceptions of research ethics experts. Finally, nine studies (n = 9) adopted a qualitative design, seven studies (n = 7) a quantitative design, and three (n = 3) a mixed-methods design.

More studies using empirical research methods are needed to better identify broader trends, to enrich discussions on the values that should govern responsible conduct of research in the academic community, and to evaluate the means by which these values can be supported in practice (Bahn, 2012 ; Beauchemin et al., 2021 ; Bruhn et al., 2002 ; Henderson et al., 2013 ; Resnik & Elliot, 2016; Sieber 2004 ). To this end, we conducted an empirical qualitative study to document the perceptions and experiences of a heterogeneous group of Canadian researchers, REB members, and research ethics experts, to answer the following broad question: What are the ethical issues in research?

Research Methods

Research design.

A qualitative research approach involving individual semi-structured interviews was used to systematically document ethical issues (De Poy & Gitlin, 2010 ; Hammell et al., 2000 ). Specifically, a descriptive phenomenological approach inspired by the philosophy of Husserl was used (Husserl, 1970 , 1999 ), as it is recommended for documenting the perceptions of ethical issues raised by various practices (Hunt & Carnavale, 2011 ).

Ethical considerations

The principal investigator obtained ethics approval for this project from the Research Ethics Board of the Université du Québec à Trois-Rivières (UQTR). All members of the research team signed a confidentiality agreement, and research participants signed the consent form after reading an information letter explaining the nature of the research project.

Sampling and recruitment

As indicated above, three types of participants were sought: (1) researchers from different academic disciplines conducting research (i.e., theoretical, fundamental or empirical) in Canadian universities; (2) REB members working in Canadian organizations responsible for the ethical review, oversight or regulation of research; and (3) research ethics experts, i.e., academics or ethicists who teach research ethics, conduct research in research ethics, or are scholars who have acquired a specialization in research ethics. To be included in the study, participants had to work in Canada, speak and understand English or French, and be willing to participate in the study. Following Thomas and Polio’s (2002) recommendation to recruit between six and twelve participants (for a homogeneous sample) to ensure data saturation, for our heterogeneous sample, we aimed to recruit approximately twelve participants in order to obtain data saturation. Having used this method several times in related projects in professional ethics, data saturation is usually achieved with 10 to 15 participants (Drolet & Goulet, 2018 ; Drolet & Girard, 2020 ; Drolet et al., 2020 ). From experience, larger samples only serve to increase the degree of data saturation, especially in heterogeneous samples (Drolet et al., 2017 , 2019 ; Drolet & Maclure, 2016 ).

Purposive sampling facilitated the identification of participants relevant to documenting the phenomenon in question (Fortin, 2010 ). To ensure a rich and most complete representation of perceptions, we sought participants with varied and complementary characteristics with regards to the social roles they occupy in research practice (Drolet & Girard, 2020 ). A triangulation of sources was used for the recruitment (Bogdan & Biklen, 2006 ). The websites of Canadian universities and Canadian health institution REBs, as well as those of major Canadian granting agencies (i.e., the Canadian Institutes of Health Research, the Natural Sciences and Engineering Research Council of Canada, and the Social Sciences and Humanities Research Council of Canada, Fonds de recherche du Quebec), were searched to identify individuals who might be interested in participating in the study. Further, people known by the research team for their knowledge and sensitivity to ethical issues in research were asked to participate. Research participants were also asked to suggest other individuals who met the study criteria.

Data Collection

Two tools were used for data collecton: (a) a socio-demographic questionnaire, and (b) a semi-structured individual interview guide. English and French versions of these two documents were used and made available, depending on participant preferences. In addition, although the interview guide contained the same questions, they were adapted to participants’ specific roles (i.e., researcher, REB member, research ethics expert). When contacted by email by the research assistant, participants were asked to confirm under which role they wished to participate (because some participants might have multiple, overlapping responsibilities) and they were sent the appropriate interview guide.

The interview guides each had two parts: an introduction and a section on ethical issues. The introduction consisted of general questions to put the participant at ease (i.e., “Tell me what a typical day at work is like for you”). The section on ethical issues was designed to capture the participant’s perceptions through questions such as: “Tell me three stories you have experienced at work that involve an ethical issue?” and “Do you feel that your organization is doing enough to address, manage, and resolve ethical issues in your work?”. Although some interviews were conducted in person, the majority were conducted by videoconference to promote accessibility and because of the COVID-19 pandemic. Interviews were digitally recorded so that the verbatim could be transcribed in full, and varied between 40 and 120 min in duration, with an average of 90 min. Research assistants conducted the interviews and transcribed the verbatim.

Data Analysis

The socio-demographic questionnaires were subjected to simple descriptive statistical analyses (i.e., means and totals), and the semi-structured interviews were subjected to qualitative analysis. The steps proposed by Giorgi ( 1997 ) for a Husserlian phenomenological reduction of the data were used. After collecting, recording, and transcribing the interviews, all verbatim were analyzed by at least two analysts: a research assistant (2nd author of this article) and the principal investigator (1st author) or a postdoctoral fellow (3rd author). The repeated reading of the verbatim allowed the first analyst to write a synopsis, i.e., an initial extraction of units of meaning. The second analyst then read the synopses, which were commented and improved if necessary. Agreement between analysts allowed the final drafting of the interview synopses, which were then analyzed by three analysts to generate and organize the units of meaning that emerged from the qualitative data.

Participants

Sixteen individuals (n = 16) participated in the study, of whom nine (9) identified as female and seven (7) as male (Table  2 ). Participants ranged in age from 22 to 72 years, with a mean age of 47.5 years. Participants had between one (1) and 26 years of experience in the research setting, with an average of 14.3 years of experience. Participants held a variety of roles, including: REB members (n = 11), researchers (n = 10), research ethics experts (n = 4), and research assistant (n = 1). As mentioned previously, seven (7) participants held more than one role, i.e., REB member, research ethics expert, and researcher. The majority (87.5%) of participants were working in Quebec, with the remaining working in other Canadian provinces. Although all participants considered themselves to be francophone, one quarter (n = 4) identified themselves as belonging to a cultural minority group.

Description of Participants

With respect to their academic background, most participants (n = 9) had a PhD, three (3) had a post-doctorate, two (2) had a master’s degree, and two (2) had a bachelor’s degree. Participants came from a variety of disciplines: nine (9) had a specialty in the humanities or social sciences, four (4) in the health sciences and three (3) in the natural sciences. In terms of their knowledge of ethics, five (5) participants reported having taken one university course entirely dedicated to ethics, four (4) reported having taken several university courses entirely dedicated to ethics, three (3) had a university degree dedicated to ethics, while two (2) only had a few hours or days of training in ethics and two (2) reported having no knowledge of ethics.

Ethical issues

As Fig.  1 illustrates, ten units of meaning emerge from the data analysis, namely: (1) research integrity, (2) conflicts of interest, (3) respect for research participants, (4) lack of supervision and power imbalances, (5) individualism and performance, (6) inadequate ethical guidance, (7) social injustices, (8) distributive injustices, (9) epistemic injustices, and (10) ethical distress. To illustrate the results, excerpts from verbatim interviews are presented in the following sub-sections. Most of the excerpts have been translated into English as the majority of interviews were conducted with French-speaking participants.

Ethical issues in research according to the participants

Research Integrity

The research environment is highly competitive and performance-based. Several participants, in particular researchers and research ethics experts, felt that this environment can lead both researchers and research teams to engage in unethical behaviour that reflects a lack of research integrity. For example, as some participants indicated, competition for grants and scientific publications is sometimes so intense that researchers falsify research results or plagiarize from colleagues to achieve their goals.

Some people will lie or exaggerate their research findings in order to get funding. Then, you see it afterwards, you realize: “ah well, it didn’t work, but they exaggerated what they found and what they did” (participant 14). Another problem in research is the identification of authors when there is a publication. Very often, there are authors who don’t even know what the publication is about and that their name is on it. (…) The time that it surprised me the most was just a few months ago when I saw someone I knew who applied for a teaching position. He got it I was super happy for him. Then I looked at his publications and … there was one that caught my attention much more than the others, because I was in it and I didn’t know what that publication was. I was the second author of a publication that I had never read (participant 14). I saw a colleague who had plagiarized another colleague. [When the colleague] found out about it, he complained. So, plagiarism is a serious [ethical breach]. I would also say that there is a certain amount of competition in the university faculties, especially for grants (…). There are people who want to win at all costs or get as much as possible. They are not necessarily going to consider their colleagues. They don’t have much of a collegial spirit (participant 10).

These examples of research misbehaviour or misconduct are sometimes due to or associated with situations of conflicts of interest, which may be poorly managed by certain researchers or research teams, as noted by many participants.

Conflict of interest

The actors and institutions involved in research have diverse interests, like all humans and institutions. As noted in Chap. 7 of the Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2, 2018),

“researchers and research students hold trust relationships, either directly or indirectly, with participants, research sponsors, institutions, their professional bodies and society. These trust relationships can be put at risk by conflicts of interest that may compromise independence, objectivity or ethical duties of loyalty. Although the potential for such conflicts has always existed, pressures on researchers (i.e., to delay or withhold dissemination of research outcomes or to use inappropriate recruitment strategies) heighten concerns that conflicts of interest may affect ethical behaviour” (p. 92).

The sources of these conflicts are varied and can include interpersonal conflicts, financial partnerships, third-party pressures, academic or economic interests, a researcher holding multiple roles within an institution, or any other incentive that may compromise a researcher’s independence, integrity, and neutrality (TCPS2, 2018). While it is not possible to eliminate all conflicts of interest, it is important to manage them properly and to avoid temptations to behave unethically.

Ethical temptations correspond to situations in which people are tempted to prioritize their own interests to the detriment of the ethical goods that should, in their own context, govern their actions (Swisher et al., 2005 ). In the case of researchers, this refers to situations that undermine independence, integrity, neutrality, or even the set of principles that govern research ethics (TCPS2, 2018) or the responsible conduct of research. According to study participants, these types of ethical issues frequently occur in research. Many participants, especially researchers and REB members, reported that conflicts of interest can arise when members of an organization make decisions to obtain large financial rewards or to increase their academic profile, often at the expense of the interests of members of their research team, research participants, or even the populations affected by their research.

A company that puts money into making its drug work wants its drug to work. So, homeopathy is a good example, because there are not really any consequences of homeopathy, there are not very many side effects, because there are no effects at all. So, it’s not dangerous, but it’s not a good treatment either. But some people will want to make it work. And that’s a big issue when you’re sitting at a table and there are eight researchers, and there are two or three who are like that, and then there are four others who are neutral, and I say to myself, this is not science. I think that this is a very big ethical issue (participant 14). There are also times in some research where there will be more links with pharmaceutical companies. Obviously, there are then large amounts of money that will be very interesting for the health-care institutions because they still receive money for clinical trials. They’re still getting some compensation because its time consuming for the people involved and all that. The pharmaceutical companies have money, so they will compensate, and that is sometimes interesting for the institutions, and since we are a bit caught up in this, in the sense that we have no choice but to accept it. (…) It may not be the best research in the world, there may be a lot of side effects due to the drugs, but it’s good to accept it, we’re going to be part of the clinical trial (participant 3). It is integrity, what we believe should be done or said. Often by the pressure of the environment, integrity is in tension with the pressures of the environment, so it takes resistance, it takes courage in research. (…) There were all the debates there about the problems of research that was funded and then the companies kept control over what was written. That was really troubling for a lot of researchers (participant 5).

Further, these situations sometimes have negative consequences for research participants as reported by some participants.

Respect for research participants

Many research projects, whether they are psychosocial or biomedical in nature, involve human participants. Relationships between the members of research teams and their research participants raise ethical issues that can be complex. Research projects must always be designed to respect the rights and interests of research participants, and not just those of researchers. However, participants in our study – i.e., REB members, researchers, and research ethics experts – noted that some research teams seem to put their own interests ahead of those of research participants. They also emphasized the importance of ensuring the respect, well-being, and safety of research participants. The ethical issues related to this unit of meaning are: respect for free, informed and ongoing consent of research participants; respect for and the well-being of participants; data protection and confidentiality; over-solicitation of participants; ownership of the data collected on participants; the sometimes high cost of scientific innovations and their accessibility; balance between the social benefits of research and the risks to participants (particularly in terms of safety); balance between collective well-being (development of knowledge) and the individual rights of participants; exploitation of participants; paternalism when working with populations in vulnerable situations; and the social acceptability of certain types of research. The following excerpts present some of these issues.

Where it disturbs me ethically is in the medical field – because it’s more in the medical field that we’re going to see this – when consent forms are presented to patients to solicit them as participants, and then [these forms] have an average of 40 pages. That annoys me. When they say that it has to be easy to understand and all that, adapted to the language, and then the hyper-technical language plus there are 40 pages to read, I don’t understand how you’re going to get informed consent after reading 40 pages. (…) For me, it doesn’t work. I read them to evaluate them and I have a certain level of education and experience in ethics, and there are times when I don’t understand anything (participant 2). There is a lot of pressure from researchers who want to recruit research participants (…). The idea that when you enter a health care institution, you become a potential research participant, when you say “yes to a research, you check yes to all research”, then everyone can ask you. I think that researchers really have this fantasy of saying to themselves: “as soon as people walk through the door of our institution, they become potential participants with whom we can communicate and get them involved in all projects”. There’s a kind of idea that, yes, it can be done, but it has to be somewhat supervised to avoid over-solicitation (…). Researchers are very interested in facilitating recruitment and making it more fluid, but perhaps to the detriment of confidentiality, privacy, and respect; sometimes that’s what it is, to think about what type of data you’re going to have in your bank of potential participants? Is it just name and phone number or are you getting into more sensitive information? (participant 9).

In addition, one participant reported that their university does not provide the resources required to respect the confidentiality of research participants.

The issue is as follows: researchers, of course, commit to protecting data with passwords and all that, but we realize that in practice, it is more difficult. It is not always as protected as one might think, because professor-researchers will run out of space. Will the universities make rooms available to researchers, places where they can store these things, especially when they have paper documentation, and is there indeed a guarantee of confidentiality? Some researchers have told me: “Listen; there are even filing cabinets in the corridors”. So, that certainly poses a concrete challenge. How do we go about challenging the administrative authorities? Tell them it’s all very well to have an ethics committee, but you have to help us, you also have to make sure that the necessary infrastructures are in place so that what we are proposing is really put into practice (participant 4).

If the relationships with research participants are likely to raise ethical issues, so too are the relationships with students, notably research assistants. On this topic, several participants discussed the lack of supervision or recognition offered to research assistants by researchers as well as the power imbalances between members of the research team.

Lack of Supervision and Power Imbalances

Many research teams are composed not only of researchers, but also of students who work as research assistants. The relationship between research assistants and other members of research teams can sometimes be problematic and raise ethical issues, particularly because of the inevitable power asymmetries. In the context of this study, several participants – including a research assistant, REB members, and researchers – discussed the lack of supervision or recognition of the work carried out by students, psychological pressure, and the more or less well-founded promises that are sometimes made to students. Participants also mentioned the exploitation of students by certain research teams, which manifest when students are inadequately paid, i.e., not reflective of the number of hours actually worked, not a fair wage, or even a wage at all.

[As a research assistant], it was more of a feeling of distress that I felt then because I didn’t know what to do. (…) I was supposed to get coaching or be supported, but I didn’t get anything in the end. It was like, “fix it by yourself”. (…) All research assistants were supposed to be supervised, but in practice they were not (participant 1). Very often, we have a master’s or doctoral student that we put on a subject and we consider that the project will be well done, while the student is learning. So, it happens that the student will do a lot of work and then we realize that the work is poorly done, and it is not necessarily the student’s fault. He wasn’t necessarily well supervised. There are directors who have 25 students, and they just don’t supervise them (participant 14). I think it’s really the power relationship. I thought to myself, how I saw my doctorate, the beginning of my research career, I really wanted to be in that laboratory, but they are the ones who are going to accept me or not, so what do I do to be accepted? I finally accept their conditions [which was to work for free]. If these are the conditions that are required to enter this lab, I want to go there. So, what do I do, well I accepted. It doesn’t make sense, but I tell myself that I’m still privileged, because I don’t have so many financial worries, one more reason to work for free, even though it doesn’t make sense (participant 1). In research, we have research assistants. (…). The fact of using people… so that’s it, you have to take into account where they are, respect them, but at the same time they have to show that they are there for the research. In English, we say “carry” or take care of people. With research assistants, this is often a problem that I have observed: for grant machines, the person is the last to be found there. Researchers, who will take, use student data, without giving them the recognition for it (participant 5). The problem at our university is that they reserve funding for Canadian students. The doctoral clientele in my field is mostly foreign students. So, our students are poorly funded. I saw one student end up in the shelter, in a situation of poverty. It ended very badly for him because he lacked financial resources. Once you get into that dynamic, it’s very hard to get out. I was made aware of it because the director at the time had taken him under her wing and wanted to try to find a way to get him out of it. So, most of my students didn’t get funded (participant 16). There I wrote “manipulation”, but it’s kind of all promises all the time. I, for example, was promised a lot of advancement, like when I got into the lab as a graduate student, it was said that I had an interest in [this particular area of research]. I think there are a lot of graduate students who must have gone through that, but it is like, “Well, your CV has to be really good, if you want to do a lot of things and big things. If you do this, if you do this research contract, the next year you could be the coordinator of this part of the lab and supervise this person, get more contracts, be paid more. Let’s say: you’ll be invited to go to this conference, this big event”. They were always dangling something, but you have to do that first to get there. But now, when you’ve done that, you have to do this business. It’s like a bit of manipulation, I think. That was very hard to know who is telling the truth and who is not (participant 1).

These ethical issues have significant negative consequences for students. Indeed, they sometimes find themselves at the mercy of researchers, for whom they work, struggling to be recognized and included as authors of an article, for example, or to receive the salary that they are due. For their part, researchers also sometimes find themselves trapped in research structures that can negatively affect their well-being. As many participants reported, researchers work in organizations that set very high productivity standards and in highly competitive contexts, all within a general culture characterized by individualism.

Individualism and performance

Participants, especially researchers, discussed the culture of individualism and performance that characterizes the academic environment. In glorifying excellence, some universities value performance and productivity, often at the expense of psychological well-being and work-life balance (i.e., work overload and burnout). Participants noted that there are ethical silences in their organizations on this issue, and that the culture of individualism and performance is not challenged for fear of retribution or simply to survive, i.e., to perform as expected. Participants felt that this culture can have a significant negative impact on the quality of the research conducted, as research teams try to maximize the quantity of their work (instead of quality) in a highly competitive context, which is then exacerbated by a lack of resources and support, and where everything must be done too quickly.

The work-life balance with the professional ethics related to work in a context where you have too much and you have to do a lot, it is difficult to balance all that and there is a lot of pressure to perform. If you don’t produce enough, that’s it; after that, you can’t get any more funds, so that puts pressure on you to do more and more and more (participant 3). There is a culture, I don’t know where it comes from, and that is extremely bureaucratic. If you dare to raise something, you’re going to have many, many problems. They’re going to make you understand it. So, I don’t talk. It is better: your life will be easier. I think there are times when you have to talk (…) because there are going to be irreparable consequences. (…) I’m not talking about a climate of terror, because that’s exaggerated, it’s not true, people are not afraid. But people close their office door and say nothing because it’s going to make their work impossible and they’re not going to lose their job, they’re not going to lose money, but researchers need time to be focused, so they close their office door and say nothing (participant 16).

Researchers must produce more and more, and they feel little support in terms of how to do such production, ethically, and how much exactly they are expected to produce. As this participant reports, the expectation is an unspoken rule: more is always better.

It’s sometimes the lack of a clear line on what the expectations are as a researcher, like, “ah, we don’t have any specific expectations, but produce, produce, produce, produce.” So, in that context, it’s hard to be able to put the line precisely: “have I done enough for my work?” (participant 3).

Inadequate ethical Guidance

While the productivity expectation is not clear, some participants – including researchers, research ethics experts, and REB members – also felt that the ethical expectations of some REBs were unclear. The issue of the inadequate ethical guidance of research includes the administrative mechanisms to ensure that research projects respect the principles of research ethics. According to those participants, the forms required for both researchers and REB members are increasingly long and numerous, and one participant noted that the standards to be met are sometimes outdated and disconnected from the reality of the field. Multicentre ethics review (by several REBs) was also critiqued by a participant as an inefficient method that encumbers the processes for reviewing research projects. Bureaucratization imposes an ever-increasing number of forms and ethics guidelines that actually hinder researchers’ ethical reflection on the issues at stake, leading the ethics review process to be perceived as purely bureaucratic in nature.

The ethical dimension and the ethical review of projects have become increasingly bureaucratized. (…) When I first started working (…) it was less bureaucratic, less strict then. I would say [there are now] tons of forms to fill out. Of course, we can’t do without it, it’s one of the ways of marking out ethics and ensuring that there are ethical considerations in research, but I wonder if it hasn’t become too bureaucratized, so that it’s become a kind of technical reflex to fill out these forms, and I don’t know if people really do ethical reflection as such anymore (participant 10). The fundamental structural issue, I would say, is the mismatch between the normative requirements and the real risks posed by the research, i.e., we have many, many requirements to meet; we have very long forms to fill out but the research projects we evaluate often pose few risks (participant 8). People [in vulnerable situations] were previously unable to participate because of overly strict research ethics rules that were to protect them, but in the end [these rules] did not protect them. There was a perverse effect, because in the end there was very little research done with these people and that’s why we have very few results, very little evidence [to support practices with these populations] so it didn’t improve the quality of services. (…) We all understand that we have to be careful with that, but when the research is not too risky, we say to ourselves that it would be good because for once a researcher who is interested in that population, because it is not a very popular population, it would be interesting to have results, but often we are blocked by the norms, and then we can’t accept [the project] (participant 2).

Moreover, as one participant noted, accessing ethics training can be a challenge.

There is no course on research ethics. […] Then, I find that it’s boring because you go through university and you come to do your research and you know how to do quantitative and qualitative research, but all the research ethics, where do you get this? I don’t really know (participant 13).

Yet, such training could provide relevant tools to resolve, to some extent, the ethical issues that commonly arise in research. That said, and as noted by many participants, many ethical issues in research are related to social injustices over which research actors have little influence.

Social Injustices

For many participants, notably researchers, the issues that concern social injustices are those related to power asymmetries, stigma, or issues of equity, diversity, and inclusion, i.e., social injustices related to people’s identities (Blais & Drolet, 2022 ). Participants reported experiencing or witnessing discrimination from peers, administration, or lab managers. Such oppression is sometimes cross-sectional and related to a person’s age, cultural background, gender or social status.

I have my African colleague who was quite successful when he arrived but had a backlash from colleagues in the department. I think it’s unconscious, nobody is overtly racist. But I have a young person right now who is the same, who has the same success, who got exactly the same early career award and I don’t see the same backlash. He’s just as happy with what he’s doing. It’s normal, they’re young and they have a lot of success starting out. So, I think there is discrimination. Is it because he is African? Is it because he is black? I think it’s on a subconscious level (participant 16).

Social injustices were experienced or reported by many participants, and included issues related to difficulties in obtaining grants or disseminating research results in one’s native language (i.e., even when there is official bilingualism) or being considered credible and fundable in research when one researcher is a woman.

If you do international research, there are things you can’t talk about (…). It is really a barrier to research to not be able to (…) address this question [i.e. the question of inequalities between men and women]. Women’s inequality is going to be addressed [but not within the country where the research takes place as if this inequality exists elsewhere but not here]. There are a lot of women working on inequality issues, doing work and it’s funny because I was talking to a young woman who works at Cairo University and she said to me: “Listen, I saw what you had written, you’re right. I’m willing to work on this but guarantee me a position at your university with a ticket to go”. So yes, there are still many barriers [for women in research] (participant 16).

Because of the varied contextual characteristics that intervene in their occurrence, these social injustices are also related to distributive injustices, as discussed by many participants.

Distributive Injustices

Although there are several views of distributive justice, a classical definition such as that of Aristotle ( 2012 ), describes distributive justice as consisting in distributing honours, wealth, and other social resources or benefits among the members of a community in proportion to their alleged merit. Justice, then, is about determining an equitable distribution of common goods. Contemporary theories of distributive justice are numerous and varied. Indeed, many authors (e.g., Fraser 2011 ; Mills, 2017 ; Sen, 2011 ; Young, 2011 ) have, since Rawls ( 1971 ), proposed different visions of how social burdens and benefits should be shared within a community to ensure equal respect, fairness, and distribution. In our study, what emerges from participants’ narratives is a definite concern for this type of justice. Women researchers, francophone researchers, early career researchers or researchers belonging to racialized groups all discussed inequities in the distribution of research grants and awards, and the extra work they need to do to somehow prove their worth. These inequities are related to how granting agencies determine which projects will be funded.

These situations make me work 2–3 times harder to prove myself and to show people in power that I have a place as a woman in research (participant 12). Number one: it’s conservative thinking. The older ones control what comes in. So, the younger people have to adapt or they don’t get funded (participant 14).

Whether it is discrimination against stigmatized or marginalized populations or interest in certain hot topics, granting agencies judge research projects according to criteria that are sometimes questionable, according to those participants. Faced with difficulties in obtaining funding for their projects, several strategies – some of which are unethical – are used by researchers in order to cope with these situations.

Sometimes there are subjects that everyone goes to, such as nanotechnology (…), artificial intelligence or (…) the therapeutic use of cannabis, which are very fashionable, and this is sometimes to the detriment of other research that is just as relevant, but which is (…), less sexy, less in the spirit of the time. (…) Sometimes this can lead to inequities in the funding of certain research sectors (participant 9). When we use our funds, we get them given to us, we pretty much say what we think we’re going to do with them, but things change… So, when these things change, sometimes it’s an ethical decision, but by force of circumstances I’m obliged to change the project a little bit (…). Is it ethical to make these changes or should I just let the money go because I couldn’t use it the way I said I would? (participant 3).

Moreover, these distributional injustices are not only linked to social injustices, but also epistemic injustices. Indeed, the way in which research honours and grants are distributed within the academic community depends on the epistemic authority of the researchers, which seems to vary notably according to their language of use, their age or their gender, but also to the research design used (inductive versus deductive), their decision to use (or not use) animals in research, or to conduct activist research.

Epistemic injustices

The philosopher Fricker ( 2007 ) conceptualized the notions of epistemic justice and injustice. Epistemic injustice refers to a form of social inequality that manifests itself in the access, recognition, and production of knowledge as well as the various forms of ignorance that arise (Godrie & Dos Santos, 2017 ). Addressing epistemic injustice necessitates acknowledging the iniquitous wrongs suffered by certain groups of socially stigmatized individuals who have been excluded from knowledge, thus limiting their abilities to interpret, understand, or be heard and account for their experiences. In this study, epistemic injustices were experienced or reported by some participants, notably those related to difficulties in obtaining grants or disseminating research results in one’s native language (i.e., even when there is official bilingualism) or being considered credible and fundable in research when a researcher is a woman or an early career researcher.

I have never sent a grant application to the federal government in English. I have always done it in French, even though I know that when you receive the review, you can see that reviewers didn’t understand anything because they are English-speaking. I didn’t want to get in the boat. It’s not my job to translate, because let’s be honest, I’m not as good in English as I am in French. So, I do them in my first language, which is the language I’m most used to. Then, technically at the administrative level, they are supposed to be able to do it, but they are not good in French. (…) Then, it’s a very big Canadian ethical issue, because basically there are technically two official languages, but Canada is not a bilingual country, it’s a country with two languages, either one or the other. (…) So I was not funded (participant 14).

Researchers who use inductive (or qualitative) methods observed that their projects are sometimes less well reviewed or understood, while research that adopts a hypothetical-deductive (or quantitative) or mixed methods design is better perceived, considered more credible and therefore more easily funded. Of course, regardless of whether a research project adopts an inductive, deductive or mixed-methods scientific design, or whether it deals with qualitative or quantitative data, it must respect a set of scientific criteria. A research project should achieve its objectives by using proven methods that, in the case of inductive research, are credible, reliable, and transferable or, in the case of deductive research, generalizable, objective, representative, and valid (Drolet & Ruest, accepted ). Participants discussing these issues noted that researchers who adopt a qualitative design or those who question the relevance of animal experimentation or are not militant have sometimes been unfairly devalued in their epistemic authority.

There is a mini war between quantitative versus qualitative methods, which I think is silly because science is a method. If you apply the method well, it doesn’t matter what the field is, it’s done well and it’s perfect ” (participant 14). There is also the issue of the place of animals in our lives, because for me, ethics is human ethics, but also animal ethics. Then, there is a great evolution in society on the role of the animal… with the new law that came out in Quebec on the fact that animals are sensitive beings. Then, with the rise of the vegan movement, [we must ask ourselves]: “Do animals still have a place in research?” That’s a big question and it also means that there are practices that need to evolve, but sometimes there’s a disconnection between what’s expected by research ethics boards versus what’s expected in the field (participant 15). In research today, we have more and more research that is militant from an ideological point of view. And so, we have researchers, because they defend values that seem important to them, we’ll talk for example about the fight for equality and social justice. They have pressure to defend a form of moral truth and have the impression that everyone thinks like them or should do so, because they are defending a moral truth. This is something that we see more and more, namely the lack of distance between ideology and science (participant 8).

The combination or intersectionality of these inequities, which seems to be characterized by a lack of ethical support and guidance, is experienced in the highly competitive and individualistic context of research; it provides therefore the perfect recipe for researchers to experience ethical distress.

Ethical distress

The concept of “ethical distress” refers to situations in which people know what they should do to act ethically, but encounter barriers, generally of an organizational or systemic nature, limiting their power to act according to their moral or ethical values (Drolet & Ruest, 2021 ; Jameton, 1984 ; Swisher et al., 2005 ). People then run the risk of finding themselves in a situation where they do not act as their ethical conscience dictates, which in the long term has the potential for exhaustion and distress. The examples reported by participants in this study point to the fact that researchers in particular may be experiencing significant ethical distress. This distress takes place in a context of extreme competition, constant injunctions to perform, and where administrative demands are increasingly numerous and complex to complete, while paradoxically, they lack the time to accomplish all their tasks and responsibilities. Added to these demands are a lack of resources (human, ethical, and financial), a lack of support and recognition, and interpersonal conflicts.

We are in an environment, an elite one, you are part of it, you know what it is: “publish or perish” is the motto. Grants, there is a high level of performance required, to do a lot, to publish, to supervise students, to supervise them well, so yes, it is clear that we are in an environment that is conducive to distress. (…). Overwork, definitely, can lead to distress and eventually to exhaustion. When you know that you should take the time to read the projects before sharing them, but you don’t have the time to do that because you have eight that came in the same day, and then you have others waiting… Then someone rings a bell and says: “ah but there, the protocol is a bit incomplete”. Oh yes, look at that, you’re right. You make up for it, but at the same time it’s a bit because we’re in a hurry, we don’t necessarily have the resources or are able to take the time to do things well from the start, we have to make up for it later. So yes, it can cause distress (participant 9). My organization wanted me to apply in English, and I said no, and everyone in the administration wanted me to apply in English, and I always said no. Some people said: “Listen, I give you the choice”, then some people said: “Listen, I agree with you, but if you’re not [submitting] in English, you won’t be funded”. Then the fact that I am young too, because very often they will look at the CV, they will not look at the project: “ah, his CV is not impressive, we will not finance him”. This is complete nonsense. The person is capable of doing the project, the project is fabulous: we fund the project. So, that happened, organizational barriers: that happened a lot. I was not eligible for Quebec research funds (…). I had big organizational barriers unfortunately (participant 14). At the time of my promotion, some colleagues were not happy with the type of research I was conducting. I learned – you learn this over time when you become friends with people after you enter the university – that someone was against me. He had another candidate in mind, and he was angry about the selection. I was under pressure for the first three years until my contract was renewed. I almost quit at one point, but another colleague told me, “No, stay, nothing will happen”. Nothing happened, but these issues kept me awake at night (participant 16).

This difficult context for many researchers affects not only the conduct of their own research, but also their participation in research. We faced this problem in our study, despite the use of multiple recruitment methods, including more than 200 emails – of which 191 were individual solicitations – sent to potential participants by the two research assistants. REB members and organizations overseeing or supporting research (n = 17) were also approached to see if some of their employees would consider participating. While it was relatively easy to recruit REB members and research ethics experts, our team received a high number of non-responses to emails (n = 175) and some refusals (n = 5), especially by researchers. The reasons given by those who replied were threefold: (a) fear of being easily identified should they take part in the research, (b) being overloaded and lacking time, and (c) the intrusive aspect of certain questions (i.e., “Have you experienced a burnout episode? If so, have you been followed up medically or psychologically?”). In light of these difficulties and concerns, some questions in the socio-demographic questionnaire were removed or modified. Talking about burnout in research remains a taboo for many researchers, which paradoxically can only contribute to the unresolved problem of unhealthy research environments.

Returning to the research question and objective

The question that prompted this research was: What are the ethical issues in research? The purpose of the study was to describe these issues from the perspective of researchers (from different disciplines), research ethics board (REB) members, and research ethics experts. The previous section provided a detailed portrait of the ethical issues experienced by different research stakeholders: these issues are numerous, diverse and were recounted by a range of stakeholders.

The results of the study are generally consistent with the literature. For example, as in our study, the literature discusses the lack of research integrity on the part of some researchers (Al-Hidabi et al., 2018 ; Swazey et al., 1993 ), the numerous conflicts of interest experienced in research (Williams-Jones et al., 2013 ), the issues of recruiting and obtaining the free and informed consent of research participants (Provencher et al., 2014 ; Keogh & Daly, 2009 ), the sometimes difficult relations between researchers and REBs (Drolet & Girard, 2020 ), the epistemological issues experienced in research (Drolet & Ruest, accepted; Sieber 2004 ), as well as the harmful academic context in which researchers evolve, insofar as this is linked to a culture of performance, an overload of work in a context of accountability (Berg & Seeber, 2016 ; FQPPU; 2019 ) that is conducive to ethical distress and even burnout.

If the results of the study are generally in line with those of previous publications on the subject, our findings also bring new elements to the discussion while complementing those already documented. In particular, our results highlight the role of systemic injustices – be they social, distributive or epistemic – within the environments in which research is carried out, at least in Canada. To summarize, the results of our study point to the fact that the relationships between researchers and research participants are likely still to raise worrying ethical issues, despite widely accepted research ethics norms and institutionalized review processes. Further, the context in which research is carried out is not only conducive to breaches of ethical norms and instances of misbehaviour or misconduct, but also likely to be significantly detrimental to the health and well-being of researchers, as well as research assistants. Another element that our research also highlighted is the instrumentalization and even exploitation of students and research assistants, which is another important and worrying social injustice given the inevitable power imbalances between students and researchers.

Moreover, in a context in which ethical issues are often discussed from a micro perspective, our study helps shed light on both the micro- and macro-level ethical dimensions of research (Bronfenbrenner, 1979 ; Glaser 1994 ). However, given that ethical issues in research are not only diverse, but also and above all complex, a broader perspective that encompasses the interplay between the micro and macro dimensions can enable a better understanding of these issues and thereby support the identification of the multiple factors that may be at their origin. Triangulating the perspectives of researchers with those of REB members and research ethics experts enabled us to bring these elements to light, and thus to step back from and critique the way that research is currently conducted. To this end, attention to socio-political elements such as the performance culture in academia or how research funds are distributed, and according to what explicit and implicit criteria, can contribute to identifying the sources of the ethical issues described above.

Contemporary culture characterized by the social acceleration

The German sociologist and philosopher Rosa (2010) argues that late modernity – that is, the period between the 1980s and today – is characterized by a phenomenon of social acceleration that causes various forms of alienation in our relationship to time, space, actions, things, others and ourselves. Rosa distinguishes three types of acceleration: technical acceleration , the acceleration of social changes and the acceleration of the rhythm of life . According to Rosa, social acceleration is the main problem of late modernity, in that the invisible social norm of doing more and faster to supposedly save time operates unchallenged at all levels of individual and collective life, as well as organizational and social life. Although we all, researchers and non-researchers alike, perceive this unspoken pressure to be ever more productive, the process of social acceleration as a new invisible social norm is our blind spot, a kind of tyrant over which we have little control. This conceptualization of the contemporary culture can help us to understand the context in which research is conducted (like other professional practices). To this end, Berg & Seeber ( 2016 ) invite faculty researchers to slow down in order to better reflect and, in the process, take care of their health and their relationships with their colleagues and students. Many women professors encourage their fellow researchers, especially young women researchers, to learn to “say No” in order to protect their mental and physical health and to remain in their academic careers (Allaire & Descheneux, 2022 ). These authors also remind us of the relevance of Kahneman’s ( 2012 ) work which demonstrates that it takes time to think analytically, thoroughly, and logically. Conversely, thinking quickly exposes humans to cognitive and implicit biases that then lead to errors in thinking (e.g., in the analysis of one’s own research data or in the evaluation of grant applications or student curriculum vitae). The phenomenon of social acceleration, which pushes the researcher to think faster and faster, is likely to lead to unethical bad science that can potentially harm humankind. In sum, Rosa’s invitation to contemporary critical theorists to seriously consider the problem of social acceleration is particularly insightful to better understand the ethical issues of research. It provides a lens through which to view the toxic context in which research is conducted today, and one that was shared by the participants in our study.

Clark & Sousa ( 2022 ) note, it is important that other criteria than the volume of researchers’ contributions be valued in research, notably quality. Ultimately, it is the value of the knowledge produced and its influence on the concrete lives of humans and other living beings that matters, not the quantity of publications. An interesting articulation of this view in research governance is seen in a change in practice by Australia’s national health research funder: they now restrict researchers to listing on their curriculum vitae only the top ten publications from the past ten years (rather than all of their publications), in order to evaluate the quality of contributions rather than their quantity. To create environments conducive to the development of quality research, it is important to challenge the phenomenon of social acceleration, which insidiously imposes a quantitative normativity that is both alienating and detrimental to the quality and ethical conduct of research. Based on our experience, we observe that the social norm of acceleration actively disfavours the conduct of empirical research on ethics in research. The fact is that researchers are so busy that it is almost impossible for them to find time to participate in such studies. Further, operating in highly competitive environments, while trying to respect the values and ethical principles of research, creates ethical paradoxes for members of the research community. According to Malherbe ( 1999 ), an ethical paradox is a situation where an individual is confronted by contradictory injunctions (i.e., do more, faster, and better). And eventually, ethical paradoxes lead individuals to situations of distress and burnout, or even to ethical failures (i.e., misbehaviour or misconduct) in the face of the impossibility of responding to contradictory injunctions.

Strengths and Limitations of the study

The triangulation of perceptions and experiences of different actors involved in research is a strength of our study. While there are many studies on the experiences of researchers, rarely are members of REBs and experts in research ethics given the space to discuss their views of what are ethical issues. Giving each of these stakeholders a voice and comparing their different points of view helped shed a different and complementary light on the ethical issues that occur in research. That said, it would have been helpful to also give more space to issues experienced by students or research assistants, as the relationships between researchers and research assistants are at times very worrying, as noted by a participant, and much work still needs to be done to eliminate the exploitative situations that seem to prevail in certain research settings. In addition, no Indigenous or gender diverse researchers participated in the study. Given the ethical issues and systemic injustices that many people from these groups face in Canada (Drolet & Goulet, 2018 ; Nicole & Drolet, in press ), research that gives voice to these researchers would be relevant and contribute to knowledge development, and hopefully also to change in research culture.

Further, although most of the ethical issues discussed in this article may be transferable to the realities experienced by researchers in other countries, the epistemic injustice reported by Francophone researchers who persist in doing research in French in Canada – which is an officially bilingual country but in practice is predominantly English – is likely specific to the Canadian reality. In addition, and as mentioned above, recruitment proved exceedingly difficult, particularly amongst researchers. Despite this difficulty, we obtained data saturation for all but two themes – i.e., exploitation of students and ethical issues of research that uses animals. It follows that further empirical research is needed to improve our understanding of these specific issues, as they may diverge to some extent from those documented here and will likely vary across countries and academic research contexts.

Conclusions

This study, which gave voice to researchers, REB members, and ethics experts, reveals that the ethical issues in research are related to several problematic elements as power imbalances and authority relations. Researchers and research assistants are subject to external pressures that give rise to integrity issues, among others ethical issues. Moreover, the current context of social acceleration influences the definition of the performance indicators valued in academic institutions and has led their members to face several ethical issues, including social, distributive, and epistemic injustices, at different steps of the research process. In this study, ten categories of ethical issues were identified, described and illustrated: (1) research integrity, (2) conflicts of interest, (3) respect for research participants, (4) lack of supervision and power imbalances, (5) individualism and performance, (6) inadequate ethical guidance, (7) social injustices, (8) distributive injustices, (9) epistemic injustices, and (10) ethical distress. The triangulation of the perspectives of different members (i.e., researchers from different disciplines, REB members, research ethics experts, and one research assistant) involved in the research process made it possible to lift the veil on some of these ethical issues. Further, it enabled the identification of additional ethical issues, especially systemic injustices experienced in research. To our knowledge, this is the first time that these injustices (social, distributive, and epistemic injustices) have been clearly identified.

Finally, this study brought to the fore several problematic elements that are important to address if the research community is to develop and implement the solutions needed to resolve the diverse and transversal ethical issues that arise in research institutions. A good starting point is the rejection of the corollary norms of “publish or perish” and “do more, faster, and better” and their replacement with “publish quality instead of quantity”, which necessarily entails “do less, slower, and better”. It is also important to pay more attention to the systemic injustices within which researchers work, because these have the potential to significantly harm the academic careers of many researchers, including women researchers, early career researchers, and those belonging to racialized groups as well as the health, well-being, and respect of students and research participants.

Acknowledgements

The team warmly thanks the participants who took part in the research and who made this study possible. Marie-Josée Drolet thanks the five research assistants who participated in the data collection and analysis: Julie-Claude Leblanc, Élie Beauchemin, Pénéloppe Bernier, Louis-Pierre Côté, and Eugénie Rose-Derouin, all students at the Université du Québec à Trois-Rivières (UQTR), two of whom were active in the writing of this article. MJ Drolet and Bryn Williams-Jones also acknowledge the financial contribution of the Social Sciences and Humanities Research Council of Canada (SSHRC), which supported this research through a grant. We would also like to thank the reviewers of this article who helped us improve it, especially by clarifying and refining our ideas.

Competing Interests and Funding

As noted in the Acknowledgements, this research was supported financially by the Social Sciences and Humanities Research Council of Canada (SSHRC).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Al-Hidabi, Abdulmalek, M. D., & The, P. L. (2018). Multiple Publications: The Main Reason for the Retraction of Papers in Computer Science. In K. Arai, S. Kapoor, & R. Bhatia (eds), Future of Information and Communication Conference (FICC): Advances in Information and Communication, Advances in Intelligent Systems and Computing (AISC), Springer, vol. 886, pp. 511–526
• Allaire, S., & Deschenaux, F. (2022). Récits de professeurs d’université à mi-carrière. Si c’était à refaire… . Presses de l’Université du Québec
• Aristotle . Aristotle’s Nicomachean Ethics. Chicago: The University of Chicago Press; 2012. [ Google Scholar ]
• Bahn S. Keeping Academic Field Researchers Safe: Ethical Safeguards. Journal of Academic Ethics. 2012; 10 :83–91. doi: 10.1007/s10805-012-9159-2. [ CrossRef ] [ Google Scholar ]
• Balk DE. Bereavement Research Using Control Groups: Ethical Obligations and Questions. Death Studies. 1995; 19 :123–138. doi: 10.1080/07481189508252720. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beauchemin, É., Côté, L. P., Drolet, M. J., & Williams-Jones, B. (2021). Conceptualizing Ethical Issues in the Conduct of Research: Results from a Critical and Systematic Literature Review. Journal of Academic Ethics , Early Online. 10.1007/s10805-021-09411-7
• Berg, M., & Seeber, B. K. (2016). The Slow Professor . University of Toronto Press
• Birchley G, Huxtable R, Murtagh M, Meulen RT, Flach P, Gooberman-Hill R. Smart homes, private homes? An empirical study of technology researchers’ perceptions of ethical issues in developing smart-home health technologies. BMC Medical Ethics. 2017; 18 (23):1–13. doi: 10.1186/s12910-017-0183-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blais, J., & Drolet, M. J. (2022). Les injustices sociales vécues en camp de réfugiés: les comprendre pour mieux intervenir auprès de personnes ayant séjourné dans un camp de réfugiés. Recueil annuel belge d’ergothérapie , 14, 37–48
• Bogdan, R. C., & Biklen, S. K. (2006). Qualitative research in education: An introduction to theory and methods . Allyn & Bacon
• Bouffard C. Le développement des pratiques de la génétique médicale et la construction des normes bioéthiques. Anthropologie et Sociétés. 2000; 24 (2):73–90. doi: 10.7202/015650ar. [ CrossRef ] [ Google Scholar ]
• Bronfenbrenner, U. (1979). The Ecology of Human development. Experiments by nature and design . Harvard University Press
• Bruhn JG, Zajac G, Al-Kazemi AA, Prescott LD. Moral positions and academic conduct: Parameters of tolerance for ethics failure. Journal of Higher Education. 2002; 73 (4):461–493. doi: 10.1353/jhe.2002.0033. [ CrossRef ] [ Google Scholar ]
• Clark, A., & Sousa (2022). It’s time to end Canada’s obsession with research quantity. University Affairs/Affaires universitaires , February 14th. https://www.universityaffairs.ca/career-advice/effective-successfull-happy-academic/its-time-to-end-canadas-obsession-with-research-quantity/?utm_source=University+Affairs+e-newsletter&utm_campaign=276a847f 70-EMAIL_CAMPAIGN_2022_02_16&utm_medium=email&utm_term=0_314bc2ee29-276a847f70-425259989
• Colnerud G. Ethical dilemmas in research in relation to ethical review: An empirical study. Research Ethics. 2015; 10 (4):238–253. doi: 10.1177/1747016114552339. [ CrossRef ] [ Google Scholar ]
• Davison J. Dilemmas in Research: Issues of Vulnerability and Disempowerment for the Social Workers/Researcher. Journal of Social Work Practice. 2004; 18 (3):379–393. doi: 10.1080/0265053042000314447. [ CrossRef ] [ Google Scholar ]
• DePoy E, Gitlin LN. Introduction to Research. St. Louis: Elsevier Mosby; 2010. [ Google Scholar ]
• Drolet, M. J., & Goulet, M. (2018). Travailler avec des patients autochtones du Canada ? Perceptions d’ergothérapeutes du Québec des enjeux éthiques de cette pratique. Recueil annuel belge francophone d’ergothérapie , 10 , 25–56
• Drolet MJ, Girard K. Les enjeux éthiques de la recherche en ergothérapie: un portrait préoccupant. Revue canadienne de bioéthique. 2020; 3 (3):21–40. doi: 10.7202/1073779ar. [ CrossRef ] [ Google Scholar ]
• Drolet MJ, Girard K, Gaudet R. Les enjeux éthiques de l’enseignement en ergothérapie: des injustices au sein des départements universitaires. Revue canadienne de bioéthique. 2020; 3 (1):22–36. [ Google Scholar ]
• Drolet MJ, Maclure J. Les enjeux éthiques de la pratique de l’ergothérapie: perceptions d’ergothérapeutes. Revue Approches inductives. 2016; 3 (2):166–196. doi: 10.7202/1037918ar. [ CrossRef ] [ Google Scholar ]
• Drolet MJ, Pinard C, Gaudet R. Les enjeux éthiques de la pratique privée: des ergothérapeutes du Québec lancent un cri d’alarme. Ethica – Revue interdisciplinaire de recherche en éthique. 2017; 21 (2):173–209. [ Google Scholar ]
• Drolet MJ, Ruest M. De l’éthique à l’ergothérapie: un cadre théorique et une méthode pour soutenir la pratique professionnelle. Québec: Presses de l’Université du Québec; 2021. [ Google Scholar ]
• Drolet, M. J., & Ruest, M. (accepted). Quels sont les enjeux éthiques soulevés par la recherche scientifique? In M. Lalancette & J. Luckerhoff (dir). Initiation au travail intellectuel et à la recherche . Québec: Presses de l’Université du Québec, 18 p
• Drolet MJ, Sauvageau A, Baril N, Gaudet R. Les enjeux éthiques de la formation clinique en ergothérapie. Revue Approches inductives. 2019; 6 (1):148–179. doi: 10.7202/1060048ar. [ CrossRef ] [ Google Scholar ]
• Fédération québécoise des professeures et des professeurs d’université (FQPPU) Enquête nationale sur la surcharge administrative du corps professoral universitaire québécois. Principaux résultats et pistes d’action. Montréal: FQPPU; 2019. [ Google Scholar ]
• Fortin MH. Fondements et étapes du processus de recherche. Méthodes quantitatives et qualitatives. Montréal, QC: Chenelière éducation; 2010. [ Google Scholar ]
• Fraser DM. Ethical dilemmas and practical problems for the practitioner researcher. Educational Action Research. 1997; 5 (1):161–171. doi: 10.1080/09650799700200014. [ CrossRef ] [ Google Scholar ]
• Fraser, N. (2011). Qu’est-ce que la justice sociale? Reconnaissance et redistribution . La Découverte
• Fricker, M. (2007). Epistemic Injustice: Power and the Ethics of Knowing . Oxford University Press
• Giorgi A, et al. De la méthode phénoménologique utilisée comme mode de recherche qualitative en sciences humaines: théories, pratique et évaluation. In: Poupart J, Groulx LH, Deslauriers JP, et al., editors. La recherche qualitative: enjeux épistémologiques et méthodologiques. Boucherville, QC: Gaëtan Morin; 1997. pp. 341–364. [ Google Scholar ]
• Giorgini V, Mecca JT, Gibson C, Medeiros K, Mumford MD, Connelly S, Devenport LD. Researcher Perceptions of Ethical Guidelines and Codes of Conduct. Accountability in Research. 2016; 22 (3):123–138. doi: 10.1080/08989621.2014.955607. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Glaser, J. W. (1994). Three realms of ethics: Individual, institutional, societal. Theoretical model and case studies . Kansas Cuty, Sheed & Ward
• Godrie B, Dos Santos M. Présentation: inégalités sociales, production des savoirs et de l’ignorance. Sociologie et sociétés. 2017; 49 (1):7. doi: 10.7202/1042804ar. [ CrossRef ] [ Google Scholar ]
• Hammell KW, Carpenter C, Dyck I. Using Qualitative Research: A Practical Introduction for Occupational and Physical Therapists. Edinburgh: Churchill Livingstone; 2000. [ Google Scholar ]
• Henderson M, Johnson NF, Auld G. Silences of ethical practice: dilemmas for researchers using social media. Educational Research and Evaluation. 2013; 19 (6):546–560. doi: 10.1080/13803611.2013.805656. [ CrossRef ] [ Google Scholar ]
• Husserl E. The crisis of European sciences and transcendental phenomenology. Evanston, IL: Northwestern University Press; 1970. [ Google Scholar ]
• Husserl E. The train of thoughts in the lectures. In: Polifroni EC, Welch M, editors. Perspectives on Philosophy of Science in Nursing. Philadelphia, PA: Lippincott; 1999. [ Google Scholar ]
• Hunt SD, Chonko LB, Wilcox JB. Ethical problems of marketing researchers. Journal of Marketing Research. 1984; 21 :309–324. doi: 10.1177/002224378402100308. [ CrossRef ] [ Google Scholar ]
• Hunt MR, Carnevale FA. Moral experience: A framework for bioethics research. Journal of Medical Ethics. 2011; 37 (11):658–662. doi: 10.1136/jme.2010.039008. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jameton, A. (1984). Nursing practice: The ethical issues . Englewood Cliffs, Prentice-Hall
• Jarvis K. Dilemmas in International Research and the Value of Practical Wisdom. Developing World Bioethics. 2017; 17 (1):50–58. doi: 10.1111/dewb.12121. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kahneman D. Système 1, système 2: les deux vitesses de la pensée. Paris: Flammarion; 2012. [ Google Scholar ]
• Keogh B, Daly L. The ethics of conducting research with mental health service users. British Journal of Nursing. 2009; 18 (5):277–281. doi: 10.12968/bjon.2009.18.5.40539. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lierville AL, Grou C, Pelletier JF. Enjeux éthiques potentiels liés aux partenariats patients en psychiatrie: État de situation à l’Institut universitaire en santé mentale de Montréal. Santé mentale au Québec. 2015; 40 (1):119–134. doi: 10.7202/1032386ar. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lynöe N, Sandlund M, Jacobsson L. Research ethics committees: A comparative study of assessment of ethical dilemmas. Scandinavian Journal of Public Health. 1999; 27 (2):152–159. doi: 10.1177/14034948990270020401. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malherbe JF. Compromis, dilemmes et paradoxes en éthique clinique. Anjou: Éditions Fides; 1999. [ Google Scholar ]
• McGinn R. Discernment and denial: Nanotechnology researchers’ recognition of ethical responsibilities related to their work. NanoEthics. 2013; 7 :93–105. doi: 10.1007/s11569-013-0174-6. [ CrossRef ] [ Google Scholar ]
• Mills, C. W. (2017). Black Rights / White rongs. The Critique of Racial Liberalism . Oxford University Press
• Miyazaki AD, Taylor KA. Researcher interaction biases and business ethics research: Respondent reactions to researcher characteristics. Journal of Business Ethics. 2008; 81 (4):779–795. doi: 10.1007/s10551-007-9547-5. [ CrossRef ] [ Google Scholar ]
• Mondain N, Bologo E. L’intentionnalité du chercheur dans ses pratiques de production des connaissances: les enjeux soulevés par la construction des données en démographie et santé en Afrique. Cahiers de recherche sociologique. 2009; 48 :175–204. doi: 10.7202/039772ar. [ CrossRef ] [ Google Scholar ]
• Nicole, M., & Drolet, M. J. (in press). Fitting transphobia and cisgenderism in occupational therapy, Occupational Therapy Now
• Pope KS, Vetter VA. Ethical dilemmas encountered by members of the American Psychological Association: A national survey. The American Psychologist. 1992; 47 (3):397–411. doi: 10.1037/0003-066X.47.3.397. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Provencher V, Mortenson WB, Tanguay-Garneau L, Bélanger K, Dagenais M. Challenges and strategies pertaining to recruitment and retention of frail elderly in research studies: A systematic review. Archives of Gerontology and Geriatrics. 2014; 59 (1):18–24. doi: 10.1016/j.archger.2014.03.006. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rawls, J. (1971). A Theory of Justice . Harvard University Press
• Resnik DB, Elliott KC. The Ethical Challenges of Socially Responsible Science. Accountability in Research. 2016; 23 (1):31–46. doi: 10.1080/08989621.2014.1002608. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosa, H. (2010). Accélération et aliénation. Vers une théorie critique de la modernité tardive . Paris, Découverte
• Sen, A. K. (2011). The Idea of Justice . The Belknap Press of Harvard University Press
• Sen, A. K. (1995). Inegality Reexaminated . Oxford University Press
• Sieber JE. Empirical Research on Research Ethics. Ethics & Behavior. 2004; 14 (4):397–412. doi: 10.1207/s15327019eb1404_9. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sigmon ST. Ethical practices and beliefs of psychopathology researchers. Ethics & Behavior. 1995; 5 (4):295–309. doi: 10.1207/s15327019eb0504_1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Swazey JP, Anderson MS, Lewis KS. Ethical Problems in Academic Research. American Scientist. 1993; 81 (6):542–553. [ Google Scholar ]
• Swisher LL, Arsalanian LE, Davis CM. The realm-individual-process-situation (RIPS) model of ethical decision-making. HPA Resource. 2005; 5 (3):3–8. [ Google Scholar ]
• Tri-Council Policy Statement (TCPS2) (2018). Ethical Conduct for Research Involving Humans . Government of Canada, Secretariat on Responsible Conduct of Research. https://ethics.gc.ca/eng/documents/tcps2-2018-en-interactive-final.pdf
• Thomas SP, Pollio HR. Listening to Patients: A Phenomenological Approach to Nursing Research and Practice. New York: Springer Publishing Company; 2002. [ Google Scholar ]
• Wiegand DL, Funk M. Consequences of clinical situations that cause critical care nurses to experience moral distress. Nursing Ethics. 2012; 19 (4):479–487. doi: 10.1177/0969733011429342. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Williams-Jones B, Potvin MJ, Mathieu G, Smith E. Barriers to research on research ethics review and conflicts of interest. IRB: Ethics & Human Research. 2013; 35 (5):14–20. [ PubMed ] [ Google Scholar ]
• Young, I. M. (2011). Justice and the Politics of difference . Princeton University Press

The Environmental Kuznets Curve (EKC) Hypothesis on GHG emissions: analyses for transportation industry of South Africa

• Open access
• Published: 27 September 2024
• Volume 5 , article number  302 , ( 2024 )

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• Oluwole Joseph Oladunni   ORCID: orcid.org/0000-0002-2718-7974 1 , 2 ,
• Oludolapo A. Olanrewaju 1 &
• Carman K. M. Lee 2  

A series of energy-econometrics techniques were employed for a 5-year time span between 2016 and 2020. The tests of Environmental Kuznets Curve (EKC) hypothesis were conducted essentially to examine the significance of economic growth (GDP), energy consumption (EC), with energy intensity (EI), and on-road passenger vehicles (PV) as related to economic development on the mitigation of carbon emissions (CO 2 - eq ) in the transportation industry of South Africa. The findings from the prevailing research imply that, with respect to South Africa’s transportation industry, CO 2 - eq emissions increased in the course of early phases of economic growth while it tends to decline at certain levels of economic threshold. Though the nation maintains the edge of turning points in both the industrial and circular economy. The results further indicate a nexus between GDP and EC, which consequently affect the CO 2 - eq emissions. The findings proffer the needs to monitor the EC from the long-run impacts alongside the short run impacts of the forecast. The per capita GDP from the short-run impacts of t-stat—(4.928) to the long run effects of t-stat—(5.033) rises, indicating its improper influence in the industry. To limit the use of fossil-based fuels, as demonstrated in the negative signal of EI for long-run impacts of a p-value (0.2835), then to the short run effects which possess a significant p-value. It also highlights the directional correlation surfacing between EC, EI and South Africa’s on-road PV. In the computation context, the series was determined to be stationary at its first differences, as evident by the R 2 combined with the R 2 (Adjusted) values of 0.9837 and 0.9827, respectively, for both long-run and short-run assessments. The indication of the research among others further reveals that public transportation systems of road and rail options, which have the potentials to incorporate alternative energy sources, can be the required efforts to mitigate climate change and global warming effects in the transportation industry.

Avoid common mistakes on your manuscript.

1 Introduction

The World Health Organization links air pollution to airborne particles that are harmful to living beings when they exceed a certain concentration threshold [ 1 ]. Greenhouse gases (GHG) are the gaseous compounds present in the atmosphere. They absorb infrared radiation and retain heat in the atmosphere, this is responsible for the greenhouse effect, eventually leading to global warming. On the necessity of economic development, owing to the expansion of economic growth, there has been a significant rise in transportation activities, industrial production, energy use and other human activities. These increased activities typically rely on the utilization of polluting energies and natural resources to the extent that economic development is frequently considered as a possible contributor to environmental degradation. In 2018, the International Energy Agency ranked the transportation industry second by virtue of its extensive reliance on fossil fuels (FFs) globally in terms of energy-related GHG and carbon dioxide (CO 2 ) emissions [ 2 ]. In the near future, road transportation, including passenger and freight vehicles, is expected to use more energy, with increase in emissions of over 50% [ 3 , 4 , 5 ].

Global concerns include the need for immediate action to mitigate GHG emissions given the increasing impact of the transportation industry on the environment [ 6 ]. The emission of GHG particularly carbon compounds, has far-reaching effects that extend beyond the surface consequence of global warming alone. In addition to the increase in respiratory and cardiovascular diseases, the number of all types of such associated diseases are also on the rise for the concerns of public health. These diseases ultimately result in a reduced lifespan for humans [ 7 ]. The transportation sector is among the principal sectors that trigger a country’s economic growth, in full measure, it impacts daily activities. However, it is strained as one of the main sources of energy consumption, resulting in environmental degradation. The discourse among researchers and experts in twenty-first century has focused on the collateral damage to our world due to the unbearable increase in carbon emissions that led to global warming from the outcome of economic developments that resulted to environmental degradation [ 8 ].

Over the course of decades, the transportation industry has relied heavily on nonrenewable energy sources, mostly fossil fuels, this has led to severe environmental effects, significant and increasing contribution to global GHG emissions [ 9 ]. The transportation industry continues to play a significant role in all the economic sectors that are main contributors to carbon emissions. It was found that the reason for the increasing energy consumption in the transport sector is the escalating increase in passenger vehicles and the increase in income earned by vehicle users. The sector as indicated in the literature, has its primary direct causes of carbon emissions from multiple dimensions of privately acquired passenger vehicles, accounting for over 700 million on-road passenger vehicles globally [ 4 , 10 ]. It is now a well-known fact, that achieving emissions’ mitigation in transportation industry is more sophisticated than realizing reductions from stationary sources [ 11 ].

This ever-increasing debate in the turn of the twenty-first century has to focus on the economic development and environmental degradation associated to carbon emissions, and consequently the global warming [ 12 , 13 ]. This necessitates global exclamation for several quarters. The global CO 2 atmospheric emissions based on the analysis conducted from NOAA’s Global Monitoring Laboratory is 414.72 parts per million [ 14 ]. Although China and the United States remain the leading emitters, Africa as accounted by records of formal inspections is found to generate fewer emissions than the rest of the world. However, worldwide carbon emissions in global temperature have now exceeded 1.26 0 C, evident by Hansen et al. [ 15 ]. Moreover, it is not only in the interests of South Africa and Africa, or any nation and, or continent; our world at large are all to take responsibility and be accountable on the bearable reduction of GHG emissions for the required lively air quality with serenity. In OECD nations the effects of transport infrastructure, economic growth, energy consumption, energy sources and carbon emissions were investigated on both short run and long run to determine the level of negative impacts of air quality and the measures to be taken to have an eco-friendly sustainable environment [ 13 , 16 , 17 ]. Countries worldwide, particularly those of developed economies, have acknowledged the importance of proper energy use with by-product emissions for optimal and strategic reductions. It is imperative to address the concerns regarding carbon emissions because emanating emissions negatively impact all forms of mortality via their influence on environmental air quality [ 18 ]. Africa carries the upset notoriety of having the highest mortality rate globally, many due to improper air quality as evidenced by available data (World Health Organization, 2018).

The primary source of energy used in the transportation sector is non-renewable energy of fossil fuel types, such as oil and gas, which discharge large amounts of GHG emissions [ 19 ]. This negatively impacts the environment and is responsible for a growing proportion of global emissions. The United Nations Conference on Trade and Development, which was established to further advance the role of the organization, stated that the transportation industry globally consumed approximately 67% of petroleum products in 2012. Based on the analysis, it has been forecasted that by the year 2035, if no drastic measures are taken, the energy consumption of fossil fuels will increase to 82%, and due to the persistent increase in passenger vehicles, its demand is deemed to rise to 78% by 2040 [ 20 ]. The outcomes surrounding these circumstances are the emissions of pollutants, particularly those of greenhouse gases present in the atmosphere, and of capable CO 2 equivalence. As it is estimated that there will be a 25% increase in CO 2 emissions only due to the combustion of fossil fuels in the transportation industry, it remains imperative to conduct research studies focusing on its mitigation. Furthermore, it is expected that CO 2 emissions will increase to 1.7% annually in industrialized emerging economies by 2030 [ 21 ], this is a better fit for concrete and genuine research engagements.

The transportation industry is one of the largest energy consumers, with increasing access of connectivity for point-to-point transfer of peoples, goods and services over the years. This has contributed to the increasing economic development. Although there is a growing demand for transportation services, it will result in increased energy consumption as the case applies, and thus the burning of fossil fuels. Consequently, this degrades air quality and the environment. The mitigation of CO 2 emissions, air pollution control due to road transport activities, energy management, and required freight management have been the prioritized objectives of sustainable ecosystems that are eco-friendly. In Africa, the highest energy consumption is still in the order of fossil fuels > coal > natural gas. Fossil fuels are frequently used in the transportation sector, whereas renewable energy usage is very minute in comparison. Although Africa is rich in clean energy sources that could better enhance air quality, however due to concerns in technological advancements and innovations, drive and will, it still relies heavily on non-renewable sources that degrade the environment [ 6 , 22 ]. By virtue of the excesses in the utilization of fossils that are consequently factored in sectorial degradation, calls have been made to curb its menace by shifting to clean renewable energy sources, thereby enhancing environmental sustainability. Previous research has demonstrated a nexus between transport energy consumption, economic growth, and carbon emissions in the transportation sector. Transportation is crucial in South Africa and has a significant impact on how daily tasks are carried out. Over the past 20 years, South Africa's population has increased to 60 million, with an economic growth rate of 2.39% from 1994 to 2022. Human population and economic growth are the main influencing factors that enhance the transportation industry, and subsequently, passenger vehicles. Saidi et al. [ 23 ] found that an increase in freight transport and per capita income played a significant role in deteriorating the quality of the environment. Nevertheless, the transportation industry is one of the main sectors in which energy consumption is at high ratio.

The transportation industry of South Africa has undergone significant growth over the years, however, this expansion has resulted in a number of environmental degradations, particularly those caused by CO 2 - eq emissions from excessive energy consumption. Estimates based on data provided by Statistics South Africa and South Africa Department of Transportation show that passenger turnover increased from 50.2 billion person-kilometres in 2010 to 152.6 billion person-kilometres in 2020, while it increased from 231.48 billion ton-km to 597 billion ton-km, for freight transportation [ 4 ]. According to the research conducted by Oladunni and Olanrewaju [ 6 ] of South Africa’s transportation industry, the energy (oil) consumption—EC of fossil fuels for the year 2020, which was estimated to be 74,498,076,377 L of kilometers covered, possesses a qualitative nexus to economic growth—in GDP of 101,659 Rand per capita. This, in turn led to the potency for energy intensity—EI of 523.359 tce/ Rand 10,000. Consequently, it produced degrading environmental impacts of around 426.3 million tons in equivalent of CO 2 emissions.

This research is pertinent as it proposes actions to enhance air and environmental quality in reducing GHGs, particularly CO 2 emissions in the transportation industry. The ultimate objective of this study is to analyze the contributions of selected environmental driving forces to carbon emissions in the transportation industry of South Africa and how they impact economic development. Furthermore, it adds to the body of literature, for which few already available on the nexus among energy consumption, its intensity, economic growth, and the required decline in carbon emissions. Consequently, the examined model's study of South Africa presents vital engineering management techniques in addressing the prevailing concerns of GHGs, and more in particular that of the CO 2 emissions for the transportation industry by adopting energy econometrics approaches.

The subsequent sections of the research are as follows: section two presents the literature of relevant studies to the present objective. The section three gives a comprehensive description of the parametric materials and variables, using the procedures that guided the study. The empirical results of the investigation are reported in the fourth section. The discussion of the findings is addressed in the fifth section, and concludes by outlining the practical implications, policy recommendations, study limitations and research gaps for further studies.

2 Literature review

With the application of diverse econometric techniques, a sizable body of literature examines the viability of the EKC hypothesis in respect to GHG emissions of different countries and regions. This resulted in variations in the estimated results [ 24 , 25 ]. Based on this hypothesis, the links between environmental pollution and economic growth per capita are in many cases (on a few exception) indicated to be inverted U-shape. This implies that working population earnings increase in tandem with economic growth. Therefore, environmental concerns will not require immediate intervention in the early stages, when environmental quality improves while the per capita income reaches the threshold known as the turning point. This hypothesis is as well demonstrated by Kang et al. 2016 [ 24 ]. Findings in the year 2016 from the studies of Kais and Sami [ 26 ] and Bilgili et al. [ 27 ] on EKC for GHG emissions show that the results depend on the type of analysis used (panel or time series) as well as the time period and geographical location that were studied. The pattern of the EKC hypothesis additionally supported by Danesh et al. [ 28 ] found that the majority of principal pollutants, including carbon monoxide (CO), nitrogen oxides (NOx), and sulfur oxides (SOx), sensed an inverted U-trajectory and supported the EKC hypothetical concept. According to Galeotti et al. [ 29 ], this link indicates multiple and mixed notions. While certain investigators observed a typical inverted U-shaped pattern, others expressed the notion that the turning point could not have been perfectly ideal [ 30 , 31 ]. Other researchers supported the findings for the existence of N-shaped correlations, as can be seen in the works of [ 32 ] and [ 33 ]. Nevertheless, the overwhelming nature of this research niche shows that economic growth does not directly translate into a long-term decrease in GHG emissions, as emissions are linked to economic expansion through energy consumption [ 34 , 35 ]. There is general agreement that rising energy consumption, which depends on the amount of energy the transportation sector uses, is the main cause of rising CO 2 emissions. However, the empirical results have shown that the evidence lacks stability because of variations in methodological approaches and for local, provincial, national, and global considerations, more specifically, the studied time period. Using statistical data from the United States, the empirical tests outcomes of [ 36 ] indicate that there is an unavoidable nexus between transportation energy consumption, income and fuels prices, one of which is a long-run relationship. The panel cointegration analysis conducted on OECD member states indicates that there is no connection between the price of gasoline, the amount of gasoline used (energy consumption), income, and car ownership in the short term. However, the results of the parametric variables demonstrate that they are connected. In a case study of the Malaysian economy, [ 37 ] analyzed some dynamic relationships between income, transportation energy consumption, and CO 2 emissions. The results demonstrate that income and transportation energy consumption are linked through the Granger causality. In their study of 107 economies, Liddle and Lung [ 38 ] evaluated the connection between per capita GDP and transport energy consumption and arrived at empirical findings that indicate that there is a long-term, positive unidirectional nexus between the two driving variables.

The Johansen cointegration results indicate that GDP impacts transportation energy consumption in the work of Achour and Belloumi [ 39 ]. However, the converse scenario is not applied in their analyses of the correlation between energy consumption and economic growth with respect to the economy of Tunisia. A generalized method of moments (GMM) was employed in the works [ 23 ] for the purpose of having a feedback confirmation on causality between transportation energy consumption and GDP of 75 nations of the world. Hence, determination was concluded. With the same methodology [ 40 ] empirically assessed the growth impact of public infrastructure under a panel of 18 OECD countries, revealing that infrastructure growth has a positive influence on labour productivity and total factor productivity. In recent years, a number of empirical studies have been conducted to better comprehend the variables of impacts on environmental quality, particularly energy consumption. Notwithstanding, there have been some attempts to shift from examining the environmental impacts of overall energy consumption to assessing the environmental effects of various energy sources, mostly non-renewable sources.

To further examine the correlation pattern for a country with a large human population, Maparu and Mazumder [ 41 ] assess the long-run causal relationship between transportation and economic growth in India. Vector Auto-regression and Vector Error Correction models were used to carry out short- and long-run causality checks, and the outcomes showed no long-run relationship. ARDL testing approach to cointegration and vector error correction model representation have been adopted to evaluate both the long-run and short-run links between economic growth, energy consumption, and carbon emissions to determine their consequential differences in impacts [ 42 , 43 ]. Rehermann et al. [ 44 ] examined the non-linear relationship between GDP per capita and transport energy consumption for countries in Latin America and the Caribbean. The findings support the N-shaped curve, while the elasticity values of transportation energy consumption with respect to GDP per capita do not demonstrate a tendency to decline over time. Sharif et al. [ 45 ] of ARDL using quantitative-on-quantitative (QQ) empirical research on the transportation-growth nexus, demonstrates that the United States’ transportation services benefit from economic growth. In addition, also with some considerations of ARDL as applied to Iran to include renewable and nonrenewable energies [ 46 ]. The fact that they serve as the driving forces for industrial development and economic growth, conversely, they lead to increase in the demand for mobility, increasing energy consumption, and intensity, investments in transportation infrastructure such as roads, highways, and bridges, and rising income levels. All of these play critical roles in the unbearable CO 2 emissions. For the purpose of achieving sustainable economic growth, [ 47 ] with [ 48 ] examined the EKC hypothesis in relation to substitution effect, growing contribution of transportation energy consumption to the resulting energy intensity and consequently the resulting GHG emissions. Energy intensity, which is a measure of a country’s energy efficiency, can be calculated either as total-factor energy efficiency or single-factor energy efficiency, as proposed by Pan et al. [ 49 ].

Being aware of how energy functions are essential, as increased energy consumption not only draws economies on track for industrialization, but also has the potential to worsen sustainability concerns [ 49 ]. Moreover, researchers continue to find it pertinent to investigate the response pattern of per capita GDP on the economy as it impacts transportation industry. The forecasts increase in GDP per capita in square or cubic functional forms can be measurable with considerable efforts. Taking into account the precepts of per capita GDP, the empirical test of [ 50 ] established long-standing assertions that adhering to environmental degradation in the short run would lead to positive environmental effects in the long run. The per capita GDP can be in its short-span increase or squared, and possibly with allowance, and then more. In EU countries, Sterpu et al. [ 51 ] investigate the validity of the EKC hypothesis by extending the per capita GDP to its quadratic [ 27 , 48 , 52 ] and cubic functional forms [ 53 , 54 , 55 ], examining the correlation between GHG emissions and per capita with the impact of energy consumption on GHGs. This is especially significant in urban areas where demand for automobile is highest [ 56 , 57 ]. In the modeled works of Gjorgievski et al. [ 58 ] as in the case of India argued that promoting nuclear energy production is the remedy to the nation’s GHG/CO 2 problems. The findings revealed that in the long term, increased nuclear energy use mitigates India’s carbon emissions. These have shown to be far-reaching evidence that one of the most important sectors for reducing carbon emissions is the transportation industry.

With respect to transportation, Alimujiang and Jiang [ 59 ] argue that energy is an essential component to maintaining economic growth, adding that an excessive reliance on fossil fuels could have two main adverse effects: (1) climate change and (2) air pollution, both of which pose threats to the planetary existence. Thus, the task of controlling air quality and climate change is critical. Nevertheless, new research endeavour are confirming the link between air pollution and global warming Zandalinas et al. [ 60 ]. Although relevant research on GHGs and CO 2 emissions from the transportation industry has made real strides, more actions are still required. Lu et al. [ 61 ] forecast future development trends for energy (oil) consumption and CO 2 emissions in the road transport industry and made recommendations to reduce intolerable oil usage. To forecast future development trends of energy consumption and GHG emissions for China and India’s road transport industry, Mittal et al. [ 62 ] created a good-fit model and assessed potential emission reduction programs. It is found that the study of China CO 2 emissions attracted lots of scrutiny. Wang et al. [ 63 ] assessed the EKC hypothesis through panel techniques by adopting provincial data of China and found the presence of a U-shaped theorem between economic growth and CO 2 emissions. On some occasions, energy intensity is assessed with convincing results to be a driving force for increasing and (primarily depending on its adaptability) mitigating CO 2 emissions and to ease the transition to low-carbon economy [ 64 ]. There is now large-scale evidence that economic development has a positive impact on the environment, while the same economic growth under loose regulatory conditions leads to increased energy consumption. As proposed by researchers, there are causal correlations between the driving forces of GHG emissions Hasan et al. [ 15 ]. The correlation between economic growth, energy consumption and intensity, passenger vehicles, and CO 2 reduction having studied by Roinioti and Koroneos, [ 65 ] demonstrates that they have both positive and negative impacts on human lives and air quality as further indicated by Khan et al. [ 66 ]. Based on these considerations, to efficiently reduce carbon emissions and enhance clean energy use, it is imperative to determine the correlation among energy consumption, its intensity, and economic growth on carbon emissions Zhao et al. [ 67 ]. Ensuing the well-known EKC framework Alataş [ 68 ], salient literary discussions have been held over the past two decades regarding the nexus between energy consumption, its intensity, and economic growth that led to deteriorating effects on air quality, which account for the increase in GHG emissions. Researchers, especially energy economics experts in the niche, have proffered that, buttressing the significance test of this hypothesis [ 69 , 70 , 71 ]. A growing body of research has looked at economic growth and energy consumption, but not simultaneously with their energy intensity to the yielding impacts of carbon emissions on the environment [ 71 , 72 ]. These studies examined developed, developing, and regional economies.

South Africa is investigated among the five developing countries examined by Sarkodie and Strezov [ 73 ] to determine the relationship between energy consumption and CO 2 emissions. Khan et al. [ 66 ] used the GMM technique to investigate the effects of energy consumption in transportation and logistics operations on environmental quality in 43 countries. Energy use and its intensities were demonstrated to determine the intensities of energy and how economic expansion affects environmental activities and the ensuing degradation in Malaysia and the OPEC countries, respectively [ 74 , 75 ]. Paramati et al. [ 76 ] applied FMOLS, CCEMG, and DOLS for their analysis to explore how energy can positively impact trade openness and economic growth in OECD countries. Further research activities reliably revealed that the ecological footprint (EF) for carbon emissions in the United States can be mitigated with controlled measures for natural resources, human capital, energy consumption, and economic growth impacts on EF of the United States. This is related to the determination of energy efficiency and its maximization for sectorial use, with respect to the transportation industry. Consequently, the outcomes of the ARDL further confirm that human capital can reduce EF, as energy consumption affects environmental deterioration [ 39 ].

In conclusion, no universally consistent nexus exist among variables, as already supported by the evidence of the EKC presented on the graph of the inverted U-shaped function, which is inconclusive. The findings have been subject to regional and national specifics, namely development path, population size and quality, economic structure, natural endowments, trade policy, and capacity of functioning institutions, as further envisaged in the empirical studies of Onafowora and Owoye [ 77 ] and Dijkgraaf and Vollebergh [ 78 ]. It can be seen that only few studies consider the presence of energy intensity in their investigative analyses, and far less considered the mixed relationship of transport energy consumption with energy intensity, that has been demonstrated in this research. As the South African transportation sector’s rising GHG or CO 2 - eq emissions becomes the focus of the present study, there exists a correlation between energy consumption, energy intensity, and economic growth. Based on previous research using similar approaches [ 4 , 6 , 20 ] the present research studies make efforts to literature by further widening the analysis of the correlations among the economic variables of impacts (–EC–EI–PV–GDP—CO 2 - eq emissions) taken from the transportation industry of South Africa as a reference case with the employment of datasets from 2016 to 2020.

3 Data and method

The 5-year dataset utilized as the parametric variables in this investigation was obtained from [ 6 ] for the nine provinces of South Africa between 2016 and 2020. The variables include the following:

Carbon emissions as per capita greenhouse gas (GHG) emissions, taken in tones of CO 2 equivalent.

Per capita gross domestic product (GDP), taken in South Africa Rand.

Per capita gross energy (oil) consumption (EC) taken in tons of oil equivalent.

Per capita energy intensity (EI), taken in Tce per 10,000 Rand.

Number of on-road passenger vehicles (PV) contributing to carbon emissions.

Taking into account the fact that the data for each province is different by characteristics in terms of population, energy use, and economic growth, it is observed that using variable per capita values will lead to significant results. To conduct these precepts, as in the case of South Africa, the nominal indices are operated over the population numbers. The employed datasets are presented in panel:

The panel dataset provides the values for the driving forces under investigation, namely EC, EI, GDP, and CO 2 - eq emissions, for the nine provinces of South Africa.

Time series data provide parametric values for each of the variables from the time period of 2016 to 2020 for each of the nine provinces of South Africa.

The data series were set up using a panel design. Data for the 2021–2023 timeframe are yet to be drawn to fit the investigation for public purposes.

In the current research analysis of EKC, three different types of empirical specifications are generally considered: (i) linear specifications, (ii) quadratic (inverted-U) specifications, and (iii) cubic (N-shaped) or sideways-mirrored (S-shaped) specifications [ 77 ]. The graph in Fig.  1 as shown illustrates the Environmental Kuznet Curve concepts and perspectives as demonstrated by the authors. This posits a correlation between the indicators of environmental degradation and economic development. It also suggests that during the early stages of industrialization and the absence of knowledge and circular economy, GHG emissions increase as environmental quality decreases. However, beyond a certain level of economic development, which varies based on different indicators, the trend reverses, with high economic growth and the inclusion of circular economy resulting in environmental improvement.

Enviromental energy-econometrics analysis of EKC hypothesis [Author’s design]

There are broad functional forms that possess additional pertinent factors, namely, external variables of time, provincial characteristics, and technical factors. The general form of the equation is as follows:

In accordance with the EKC specifications provided above, this study examines CO 2 - eq emissions (Q) as the dependent variable, per capita yearly GDP (Y) as the independent variable, time period (t) as a factor, and the explanatory variables (X). Furthermore, ɛ represents the random error component, and a i denotes the coefficients of the model, which can also be referred to as the marginal propensity for emissions. Upon conducting the EKC analyses for the three specifications, several technical details can be discerned:

IF ( →) a 1  >  0— linearity of correlation around GDP with CO 2 - eq emissions. [a 1 must be significant]

IF ( ↔)  a 1  <  0— monotonic decrease linkage around GDP and CO 2 eq  emissions. [a 1  must be significant]

IF a 1  >  0 , a 2  <  0 & a 3  =  0— quadratic linkage around GDP and CO 2 - eq  emissions. [Equilibria to be reached]

This is to evaluate the existence of an EKC-type nexus between CO 2 - eq emissions, economic growth, and the impact of energy consumption on CO 2 - eq emissions in the transportation industry, employing two energy-econometrics’ models as the basis for further analyses. To measure the environmental impacts, we use CO 2 - eq emissions as a dependent variable, while GDP, and EC, EI and PV are taken as the independent, controlling independent variables, respectively as the case applies.

3.2.1 Model 1

Taken as the first model, we employ quadratic to perform test on the EKC hypothesis as follow:

where Q corresponds to CO 2 - eq emissions, Y is the GDP per capita, X 1 … X n are the covariate explanatory variables. The ɛ it represents the error term, i denotes the provinces of South Africa while t is the time period. Other studies have employed similar approach, however, with different explanatory driving factors [ 78 ].

3.2.2 Model 2

Using the cubic equation, we applied the second model to conduct tests on the N-Shape hypothesis for the Kuznets curve as demonstrated:

The order of representations are as specified in Eq. ( 1 ). There are other researchers who employed a cubic model similar to the one utilized in this study due to their close proximity [ 48 ]. The parametric variables are taken in their logarithmic transform. The sign for coefficients of Y , Y 2 , Y 3  applied to economic growth and the specific correlations among them regulate the shape of the approximating surface.

We employed the ARDL bounds testing approach to reconfirm the presence of EKC and cointegration of variables as proposed by [ 79 ]. Eq. ( 5 ) fully remodeled in Eq. ( 6 ) from [ 48 ] background as:

where Δ denotes variable’s first difference operator, P stands for lag lengths. To use ARDL we first demonstrate cointegration among the variables. To proceed, the null hypothesis test of no cointegration is conducted against the alternative hypothesis in this other of format:

F-statistic is inculcated with respect to the series being integrated either at I(0) or I(1). As the case applies, if the F-statistic value is greater than the upper bound value, there exists cointegration among the variables. If the F-statistic value is below the crucial lower bound value, the acceptance of null hypothesis that there is no cointegration is observed, as no precision will be made following that F-statistic lies between upper and the lower bound values [ 52 ]. For the study’s validation, the critical and F-statistic values are selected by applying cointegration technique as put forward by [ 52 ]. The estimates of the short run coefficients are obtained by ( P ) whilst the long run dynamics are estimated with the coefficients \(\vartheta_{1} ,\vartheta_{2} ,\vartheta_{3} ,\vartheta_{4} ,\vartheta_{5} ,\vartheta_{6}\) as expressed in Eq. ( 6 ). The ARDL bound testing approach is an effective method for simultaneously determining better estimates of both short-run and long-run dynamics. It achieves this through a modest linear transformation, which provides a superior approach for obtaining more accurate estimates. To assess the robustness entirety of the empirical models, diagnostic tests on heteroskedasticity, normality and autocorrelation tests are conducted, thereby running the validity and consistency of the long run dynamics. This is carried using canonical cointegration regression, dynamic ordinary least square (DOLS), and modified least square (FMOLS).

Modeling the data to be analyzed [ 6 ], and the time span of 5 years in real terms along with their provincial locations are illustrated in Fig.  2 a, and b respectively as shown:

a South Africa’s driving forces impacts on GHG/CO 2 - eq emissions in transportation industry, 2016–2020. b. South Africa’s driving forces impacts on GHG / CO 2 - eq emissions in transportation industry, 2016–2020

4 Empirical results and analysis

The study investigates how economic growth in GDP per capita and its extensions, CO 2 - eq emissions, energy consumption, its yielding energy intensity and the on-road passenger vehicles cointegrate to bring forth the observable environmental impacts in the transportation industry of South Africa. We applied ARDL bound testing method in achieving this and to also prevent spurious regression. It is essential to examine the order of integration prior to ARDL bound testing method. Augmented Dickey-Fuller (ADF) and Philips Pearson (PP) tests are employed in attaining the consequential values in order to proceed. The findings of both ADF and PP indicate that none of the series is stationary at Level, as illustrated in Table  1 . Hence, the hypothesis of no stationary is rejected, as it implies that all the variables are integrated at first difference. The results further show that none of the variables is integrated at I(2). By the revealing response, the ARDL bounding technique is found appropriate.

When it has been demonstrated that none of the variables are integrated in the order I(2), the cointegration between them is further evaluated. The decision is prerequisite prior assessments of parametric variables for cointegration. To begin with, unrestricted VAR models are utilized and to subsequently identify the optimal lag length of 2 using SIC criterion. The optimal lag length of 2 adjustments is imperative at the selection of the optimal length. Thereafter, proceeding to find adjustments from the parametric variables. In confirming the cointegration, Wald test is applied to determine the value of F-statistic. The findings of Table  2 reveal the rejection of null hypothesis on the condition that no cointegration on the modeled equations.

Johansen cointegration test is employed to verify the validity of the F-statistic as generated by Wald test performance. In conducting Johansen cointegration, Trace statistics with Eigen-values were obtained. The relevance of Trace statistics and that of Eigen-values demonstrates the cointegration correlation among the investigated parametric variables. These are as presented in Table  3 in which the analyses made it evident that at the very least, cointegration correlations exist. Hence, Johansen cointegration results validate Wald statistics. Both long run and short run estimates for Eq. ( 6 ) were conducted to determine the level of significance of the exogenous variable of CO 2 - eq emissions and the underlying independent variables. In Table  4 , it can be observed that all the coefficients possess the responsive signs. More so, all the series are made significant at 0.05% level. In other words, the indication of GDP positive-path coefficient buttress that CO 2 - eq emissions in the transportation industry surfaces with increasing economic growth in both forecasts for long run and the short run.

In contrast as revealed in Table  4 , there are strong indications of long run and short run correlations among economic growth in square and cubic forecast with CO 2 - eq emissions found possessing negative sign coefficients. The implication as derived, implies that CO 2 - eq emissions in South Africa’s transportation industry increases at the early industrial phase of economic growth and fall after reaching certain level of economic expansion. The investigation validates the U-shaped EKC hypothesis in South Africa relating to transportation sector. The findings are related to [ 42 ] who conducted such line of analyses to confirm the existence of EKC in Italy, and in Turkey by [ 80 ], more so, in OECD countries [ 13 , 16 , 17 ].

With the peculiar case of South Africa transportation industry in the staggering amount of energy (oil) consumed, the on-road passenger vehicles, energy in oil consumption with its intensity are integrated in the model. This offers new directions to mitigate carbon emissions on the level at which South Africa’s economic development has reached from the general interpretation for the U-shaped EKC hypothesis, a level being depicted in Fig.  3 . It is observed that from the level at which energy are used in the transportation sector of South Africa, Energy consumption as inspected with its intensity contributes to the emissions of GHGs/CO 2 - eq  in South Africa. On the other hand, although in the long run passenger vehicles do not reveal a negative impact, however, in the scale of short run it possesses a sensitive negative impact. The demonstrated energy-econometrics analysis implies that increasing passenger vehicles (IC Engines) concurrently lead to increase in energy (oil) consumption vis-à-vis energy intensity.

Plot-trends correlations between economic growth and CO 2 - eq emissions in SA transport

The findings of the study relate to that of Zhao et al. [ 67 ] with a similar outcomes for China. Based on the research conducted, it was discovered that in numerous instances, public transportation alternatives are more eco-friendly for South Africa's transportation systems than the high volume of passenger vehicle traffic, which was found to be one of the primary contributors to CO 2 - eq emissions in the transportation industry.

This is proved viable as South Africa heavily relies on conventional (fossil) energy sources such as oil and coal, particularly oil (in fossil) for its transportation activities. Already, well over 90% of the energy use in the transportation industry is fossil-based fuels.

To check the capacity of the analyses, the resulting model of Eq. ( 6 ) is assessed by employing three different techniques, namely, fully modified least squares (FMOLS), dynamic least squares (DOLS), and canonical cointegration regression (CCR), purposed to examine the validity and reliability of the obtained outcomes through the ARDL bound test approaches [ 80 ]. The findings of Table  5 indicate that whilst economic growth possess a positive and significant impact on CO 2 - eq emissions, the square and cubic of economic growth ( GDP 2 and GDP 3 ) have negative significant impacts. This is the implied case applied to the transportation industry of South Africa. In addition, from the analytical interpretations being sensitive of the GDP flow-line, it can be rewarding to improve eco-friendly environment. Ultimately, the results of the ARDL bound test approach applied under three distinct techniques support the findings of the research which are further presented in Table  5 .

The CUSUM and CUSUMsq are performed with high sensitivity to verify the lack of structural invariance, endogeneity tests, and the reliability and stability of the models for both long and short run estimations. The results are graphically presented in Fig.  4 a and Fig.  4 b. The assessed stability diagnostics for both tests largely reside between the critical (red) lines; this implies that the model can be put forward for policy recommendations with respect to the availability of the data employed. They are found fit. It is noteworthy that, based on the most current literature, this research is considered to be forthcoming in South Africa and the continent of Africa. In Fig.  4 b, it can be observed that the data as they were not readily available from a single source of a database.

a Trend-plot for cummulative sum of recursive residual at critical bound of 5% significance. b  Trend-plot for cummulative square of recursive residual at critical bound of 5% significance

The readings may not be efficient and robust enough, however optimum determination has been exercised. Figure  4 b can only further implies that at the readings of 5% level of significance CUSUMsq for high sensitivity can be determined (as it also travels between red-lines indicator) with respect the prevailing empirical analysis.

As presented in Fig.  5 a, both the short-run and long-run impacts are identified. The parametric variables taken into consideration are contingent on the interpretations of EKC hypothesis in the derived models of energy econometrics technique for the South Africa's transportation sector. This serves as the underlying approach of the research studies. The interpretation of the readings depicts that for both long and short runs, indications exist for the correlation between economic growth in GDP per capita ( A region) and GHG emissions ( E ) of the transportation industry. As Energy Intensity (in the C” region) has to bridge the gap, there also exists a causal relationship between Energy Consumption—EC in the B’ region and Passenger Vehicles—PV in the D region. Along the axis of A-D-E in the composition of GDP, PV and GHGs there exist an indication of causal interconnection between the variables as evidently provided in empirical analyses of the 5-year employed dataset of transportation industry of South Africa.

a Acyclic model indicator of parametric variables-flow on EKC hypothetical analysis. b Directional linkages of resource-controls among selected driving forces over GHG/CO- eq emissions

Figure  5 b, as indicated, conveys the relational linkages of variables’ controls that exist among the selected driving forces impacting on GHG emissions in the transportation sector of South Africa. In line with the analyses performed on the hypothetical EKC, it can be deduced that the outcomes of GHG emissions are well dependent on variable’s computational inputs both quantitatively and qualitatively. As investigated for all the nine provinces of South Africa with time period of five years spanning from 2016 to 2020. The alterations and the adjustments of one or two or more of the endogenous variables can significantly lead to the required environmental outcomes.

5 Conclusion and policy implication

5.1 policy implications for reducing ghg emissions in transportation.

The study delves into the concept of energy econometrics complexity and applies the Environmental Kuznets Curve hypothesis, commonly utilized to analyze the nexus between economic development and environmental quality. South Africa is still a developing nation, despite being more developed of Africa's member states. In line with its Paris Agreement obligations, South Africa has been determined to further reduce its transportation sector GHG emissions from the 10-year of 60MtCO 2 - eq . This value serves as the share of the tranportation industry from the South Africa’s overall contribution of 1.2% of the world’s GHG emissions, totaling 8.08 billion metric tons in CO 2 equivalent, globally. Identifying the pattern of the Environmental Kuznets Curve (EKC) hypothesis as it pertains to economic development and environmental degradation for the requisite air quality is essential for assessing the impacts of the driving forces in the industry that contribute to carbon emissions.

Provinces in South Africa should be cognizant of their respective stages of economic development, energy use, and GHG emissions, particularly that of transportation sector. They should make targeted advances in economic development while effectively mitigating GHG emissions. At present, all the provinces are in the rising stage and have not yielded to the turning point of the interpreted EKC. The rising economic development due to carbon intensive energy is the primary reason for the accounted carbon emissions in the industry. Although, significant environmental degradation has been recorded, the nation still requires a lot of transportation systems to move people, goods, and services, nevertheless, those with sustainable air quality. According to the analysis of the development trend of transportation in various cities and provinces of South Africa at this stage, there are still problems of energy sources for both renewable and nonrenewable, and that of transportation means and modes. This, obviously lack transportation’s economic development objevtives. By updating the economic structure and controlling the development of transportation systems reasonably, a high developed economy with low carbon emissions can be achieved.

With a measure of controlling unbearable population and decreasing mortality rates while improving technological innovations, the effect of passenger vehicles on traffic emissions can be restrained. More to this effect, passengers can be guided on individual and personal benefits of choosing clean and green travel options. The transportation pricing index has the potential to be a significant factor in reducing carbon emissions. Altering consumption approach can be a viable strategy for achieving balance between the economy and the environment that will lead to sustainable development.Furthermore, the government should promote and incentivize the use of environmentally-friendly modes of transportation and the use of clean products among local residents. Modifying the cost of transportation services is an essential measure that can influence people's travel choices and subsequently impact the energy demand and carbon emissions in the transportation industry. Efforts should be made to enhance the affordability and convenience of public transportation in order to change people's preconceived notions about travel. Highway transportation is among the passenger travel that should be considered, particularly intercity and city buses, which mostly relies on fossil fuels for passenger transit. By so doing, the use of energy for automobile will predominantly shift towards natural gas and electricity. The government has the ability to diminish individuals' reliance on gasoline-powered vehicles by implementing tax policies, fuel surtaxes, and vehicle purchase taxes that are tailored to particular vehicle types. Furthermore, providing policy assistance for environmentally friendly automobile manufacturers to foster the growth of automobile industry that is clean. Offering of incentives and benefits to consumers who purchase such automobiles, can encourage individuals who use private cars to transition to a more environmentally friendly mode of transportation with reduced carbon emissions. To encourage long-distance of on-road travel that is environmentally friendly, it is important to build gas stations and charging infrastructures along the highway. This will gradually shift people's transportation practices and promote the development of low-carbon traffic in South Africa transportation sector and elsewhere.

It is important to note that there are limitations and gaps in the research. This study investigates the nexus between economic development and environmental degradation, specifically focusing on the income-emissions aspect of the EKC hypothesis in the transportation industry of South Africa. Due to limitations in data and geographic scope, our analysis is restricted to the nine provinces of South Africa over a five-year period from 2016 to 2020. Additional research investigations have the potential to broaden the temporal scope and increase the number of countries examined. Moreover, it has the capability to examine many sectors or industries both independently, and as integrated concerns, resulting in changes to the methods, and scale of the tests and diagnostics, which will ultimately lead to more outcomes.

5.2 Conclusion

Using energy econometrics techniques, this study investigates the effects of economic growth (GDP per capita), with it being squared and cubic, followed by energy consumption (EC), energy intensity (EI), and on-road passenger vehicles (PV) on the mitigation of GHG emissions in CO 2 equivalence for the transportation industry of South Africa. A five-year dataset spanning from 2016 to 2020, as it appears in Fig.  2 a, and b are adopted. The year can further be extended, only to portray an extension for subsequent forecasts. The study examined South Africa's nine provinces, considering their varying rates of economic development and dependence on fossil fuels for energy in the sector across all the provinces.

From the study period of 2016 to 2020 as 2021 only being the model’s extension forecast, South Africa’s per capita GDP ranges from R71, 920.00 to 101,659.00. The country’s energy (in oil) consumption (EC) from 2016 to 2020 is estimated ranging from 6,925,070,093 to 7,799,172,128 L and in conversion it tallies between 39.850 to 41.039 metric tons of oil consumption in energy content. The energy intensity (EI) for the study periods is within the range of 513 Tce per R10, 000.00 to 537 Tce per R10, 000.00 from 2016 to 2020 as estimated. South Africa’s on-road passenger vehicles for the research period of 2016 to 2020 are taken in units of vehicle population within the range of 11,964,234 and 12,701,630 of vehicle units. Considering the energy-econometrics debates around the EKC hypothesis sectioned into four main categories, namely; cointegration of the parametric variables, endogeneity concerns, simultaneity and omission bias for variables, the prevailing econometrics instruments are employed in the peculiar case of South Africa’s transportation industry.

In the context of South Africa, economic growth in GDP (inculcating GDP 2 and GDP 3 ), energy consumption with its intensity, and on-road passenger vehicles are modeled on CO 2 - eq emissions. In the investigation, the EKC test is employed to South Africa’s industry using the aforementioned variables as explanatory while taking CO 2 - eq emissions as the dependent variables. With the outcome of the prevailing research, CO 2 - eq emissions in South Africa’s transportation industry grew through the early phases of its economic expansion at specific level of economic threshold. To clarify the research main contribution, the study further demonstrates the directional nexus among South Africa’s on-road PV, EC, EI and per capita GDP with its excesses which is negatively probable, especially the economic expansion by a cubic scenario. The ARDL bound test approach was employed to analyze the cointegration correlation among the parametric variables. For the high performance of the model, three high-powered techniques were employed to examine the accuracy and reliability of the results from the ARDL bound test approach: FMOLS, DOLS and CCR, respectively.

Quantitatively, the series were determined to be stationary at their first differences, as indicated by R 2 and R 2 ( Adjusted ) values of 0.9837 and 0.9827 , respectively, for both long-run and short-run estimations. From the deductions of the findings, it is imperative to monitor the reactions of EC on the long effects of the t-stat —( 0.393 ) and p-value —( 0.6947 ) alongside the short run forecast impact of the t-stat —( 0.383 ) and p-value —( 0.7019 ). From the short run effects shown in the t-stat—( 4.928 ) with p-value ( 0.0000 ) to the long run effects demonstrated with p-value ( 0.0000 ), the per capita GDP increases, indicating its improper influence in the sector. Limiting the burning of fossil fuels is essential as shown by the negative signal of EI for the short run impacts of t-stat ( -1.100 ) with p-value ( 0.0000 ) and the long- run impacts shown of t-stat ( -1.076 ) with p-value ( 0.2835 ).

From these analyses, the following conclusions have been drawn:

There are implications of Environmental Kuznets curve (EKC) hypothesis in the significance of economic growth, energy consumption with its intensity, and on-road passenger vehicles in the transportation industry of South Africa.

Economic growth has a significant positive impact over GHG/CO 2 - eq emissions provided that it is checked without spanning out of control.

In both the long and short run paths, energy intensity can have significant positive impacts in South Africa.

Under proper investigation, the neutrality hypothesis is confirmed, as a correlation exist between CO 2 - eq emissions and economic growth which at large contribute to economic development.

There is also evidence of proportional nexus between the energy consumption and passenger vehicles with CO 2 - eq emissions in the transportation industry of South Africa.

In line with the outcomes of the research studies, it can be put forward for decision making, that there are convincing revelations between per capita economic growth and energy (oil) consumption that led to CO 2 - eq emissions. Automobiles that are IC-Engines running on fossil fuels should be minimized in order to contribute to the efforts of mitigating the impacts of climate change. By doing so, the mass transit can be cushioned. In addition, South Africa’s GHGs intensity can be mitigated by further enhancing renewables in the energy mix. To further support an eco-friendly environment, decision and policy makers should support alternative energy transport vehicles to limit the consumption of fossils.

Based on the accounts of this study, the following implied  knowledge among others are derived:

First, South Africa can further restructure the transportation industry to develop in a more sustainable ways, as its impacts on the environment are significantly dominant. Similarly, developing countries as a case with South Africa can focus on how their transportation systems and economic development affect environmental degradation to fully achieve intergovernmental sustainability goals, such as the ones outlined by the United Nations. For instance, that of the sustainable development goals. Consequently, this can further align South Africa's policies framing with those that are highly developed.

Furthermore, in the era of information age, the structure of the economy can be enhanced to move from carbon intensive energy to knowledge and circular economies. Notwithstanding their complexities, they are reliable path to post-industrial economy. Passenger vehicles contribute significantly to South Africa's total vehicle fleet GHG emissions. However, with rigorous fuel economy standards and increasing use of hybrid and electric vehicles, this share can be expected to decline over time as indicated by the EKC. To achieve sustainable development, it is imperative that governmental bodies prioritize policies targeting commercial vehicles, with particular emphasis on passenger on-road vehicles, in domains such as fuel economy regulations and electric vehicle (EV) deployment. Incentive-based regulations for hybrid and EV passenger vehicles can facilitate the production of cleaner energy and promote sustainable development.

Data availability

The data used to support this research is included within the manuscript. However, upon request, additional sources that involve analyses can be provided.

Comunian S, et al. Air pollution and COVID-19: the role of particulate matter in the spread and increase of COVID-19’s morbidity and mortality. Int J Environ Res Public Health. 2020;17(12):4487.

Article   CAS   Google Scholar  

Moriarty P, Honnery D. Renewable energy in an increasingly uncertain future. Appl Sci. 2022;13(1):388.

Article   Google Scholar  

Raza MY, Lin B. Decoupling and mitigation potential analysis of CO2 emissions from Pakistan’s transport sector. Sci Total Environ. 2020;730: 139000.

Oladunni OJ, Mpofu K, Olanrewaju OA. Greenhouse gas emissions and its driving forces in the transport sector of South Africa. Energy Rep. 2022;8:2052–61.

Azam A, et al. Analyzing the effect of natural gas, nuclear energy and renewable energy on GDP and carbon emissions: A multi-variate panel data analysis. Energy. 2021;219: 119592.

Oladunni OJ, Olanrewaju OA. Effects of the impact factors on transportation sector’s CO2-eq emissions: panel evaluation on South Africa’s major economies. Atmosphere. 2022;13(10):1705.

Hoegh-Guldberg O, et al. The human imperative of stabilizing global climate change at 15 C. Science. 2019;365(6459):eaaw6974.

Verma K, Pandey J. Collateral implications of carbon and metal pollution on carbon dioxide emission at land-water interface of the Ganga River. Environmental Science and Pollution Research, 2022: p. 1–16.

Holechek JL, et al. A global assessment: can renewable energy replace fossil fuels by 2050? Sustainability. 2022;14(8):4792.

Raza SA, Shah N, Sharif A. Time frequency relationship between energy consumption, economic growth and environmental degradation in the United States: evidence from transportation sector. Energy. 2019;173:706–20.

Stanley J, et al. Reducing Australian motor vehicle greenhouse gas emissions. Transport Res Part A Policy Pract. 2018;109:76–88.

Li P, Zhao P, Brand C. Future energy use and CO2 emissions of urban passenger transport in China: a travel behavior and urban form based approach. Appl Energy. 2018;211:820–42.

Taghvaee VM, Nodehi M, Saboori B. Economic complexity and CO2 emissions in OECD countries: sector-wise Environmental Kuznets Curve hypothesis. Environ Sci Pollut Res. 2022;29(53):80860–70.

Hall BD, et al. Revision of the world meteorological organization global atmosphere watch (WMO/GAW) CO 2 calibration scale. Atmos Meas Tech. 2021;14(4):3015–32.

Hasan MA, et al. Emissions from the road transport sector of New Zealand: Key drivers and challenges. Environ Sci Pollut Res. 2019;26:23937–57.

Mujtaba G, Shahzad SJH. Air pollutants, economic growth and public health: implications for sustainable development in OECD countries. Environ Sci Pollut Res. 2021;28:12686–98.

Churchill SA, et al. Transport infrastructure and CO2 emissions in the OECD over the long run. Transp Res Part D: Transp Environ. 2021;95: 102857.

Lin B, et al. Is the environmental Kuznets curve hypothesis a sound basis for environmental policy in Africa? J Clean Prod. 2016;133:712–24.

Duan H, et al. Achieving China’s energy and climate policy targets in 2030 under multiple uncertainties. Energy Economics. 2018;70:45–60.

Anwar A, Ahmad N, Madni GR. Industrialization, freight transport and environmental quality: evidence from belt and road initiative economies. Environ Sci Pollut Res. 2020;27:7053–70.

He J, et al. Towards carbon neutrality: a study on China’s long-term low-carbon transition pathways and strategies. Environ Sci Ecotechnol. 2022;9: 100134.

da Silva PP, Cerqueira PA, Ogbe W. Determinants of renewable energy growth in Sub-Saharan Africa: Evidence from panel ARDL. Energy. 2018;156:45–54.

Saidi K, Hammami S. The impact of energy consumption and CO2 emissions on economic growth: Fresh evidence from dynamic simultaneous-equations models. Sustain Cities Soc. 2015;14:178–86.

Kang Y-Q, Zhao T, Yang Y-Y. Environmental Kuznets curve for CO2 emissions in China: a spatial panel data approach. Ecol Ind. 2016;63:231–9.

Jaunky VC. The CO2 emissions-income nexus: evidence from rich countries. Energy Policy. 2011;39(3):1228–40.

Kais S, Sami H. An econometric study of the impact of economic growth and energy use on carbon emissions: panel data evidence from fifty eight countries. Renew Sustain Energy Rev. 2016;59:1101–10.

Bilgili F, Koçak E, Bulut Ü. The dynamic impact of renewable energy consumption on CO2 emissions: a revisited Environmental Kuznets Curve approach. Renew Sustain Energy Rev. 2016;54:838–45.

Miah MD, Masum MFH, Koike M. Global observation of EKC hypothesis for CO2, SOx and NOx emission: a policy understanding for climate change mitigation in Bangladesh. Energy Policy. 2010;38(8):4643–51.

Galeotti M, Manera M, Lanza A. On the robustness of robustness checks of the environmental Kuznets curve hypothesis. Environ Resource Econ. 2009;42:551–74.

Apergis N, Ozturk I. Testing environmental Kuznets curve hypothesis in Asian countries. Ecol Ind. 2015;52:16–22.

Farhani S, Ozturk I. Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environ Sci Pollut Res. 2015;22:15663–76.

Friedl B, Getzner M. Determinants of CO2 emissions in a small open economy. Ecol Econ. 2003;45(1):133–48.

Moomaw WR, Unruh GC. Are environmental Kuznets curves misleading us? The case of CO2 emissions. Environ Dev Econ. 1997;2(4):451–63.

Chen P-Y, et al. Modeling the global relationships among economic growth, energy consumption and CO2 emissions. Renew Sustain Energy Rev. 2016;65:420–31.

Shahbaz M, et al. Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries? Energy Economics. 2015;51:275–87.

Liddle B. Long-run relationship among transport demand, income, and gasoline price for the US. Transp Res Part D: Transp Environ. 2009;14(2):73–82.

Azlina A, Law SH, Mustapha NHN. Dynamic linkages among transport energy consumption, income and CO2 emission in Malaysia. Energy Policy. 2014;73:598–606.

Liddle B, Lung S. The long-run causal relationship between transport energy consumption and GDP: Evidence from heterogeneous panel methods robust to cross-sectional dependence. Econ Lett. 2013;121(3):524–7.

Achour H, Belloumi M. Investigating the causal relationship between transport infrastructure, transport energy consumption and economic growth in Tunisia. Renew Sustain Energy Rev. 2016;56:988–98.

Farhadi M. Transport infrastructure and long-run economic growth in OECD countries. Transp Res Part A Policy Pract. 2015;74:73–90.

Maparu TS, Mazumder TN. Transport infrastructure, economic development and urbanization in India (1990–2011): Is there any causal relationship? Transp Res Part A Policy Pract. 2017;100:319–36.

Bento JPC, Moutinho V. CO2 emissions, non-renewable and renewable electricity production, economic growth, and international trade in Italy. Renew Sustain Energy Rev. 2016;55:142–55.

Jebli MB, Youssef SB. The environmental Kuznets curve, economic growth, renewable and non-renewable energy, and trade in Tunisia. Renew Sustain Energy Rev. 2015;47:173–85.

Rehermann F, Pablo-Romero M. Economic growth and transport energy consumption in the Latin American and Caribbean countries. Energy Policy. 2018;122:518–27.

Sharif A, Shahbaz M, Hille E. The transportation-growth nexus in USA: fresh insights from pre-post global crisis period. Transp Res Part A Policy Pract. 2019;121:108–21.

Taghvaee VM, Mavuka C, Shirazi JK. Economic growth and energy consumption in Iran: an ARDL approach including renewable and non-renewable energies. Environ Dev Sustain. 2017;19:2405–20.

Liobikienė G, Butkus M. Environmental Kuznets Curve of greenhouse gas emissions including technological progress and substitution effects. Energy. 2017;135:237–48.

Bölük G, Mert M. Fossil & renewable energy consumption, GHGs (greenhouse gases) and economic growth: Evidence from a panel of EU (European Union) countries. Energy. 2014;74:439–46.

Pan X, et al. Dynamics of financial development, trade openness, technological innovation and energy intensity: evidence from Bangladesh. Energy. 2019;171:456–64.

Panayotou T. Empirical tests and policy analysis of environmental degradation at different stages of economic development. 1993.

Sterpu M, Soava G, Mehedintu A. Impact of economic growth and energy consumption on greenhouse gas emissions: testing environmental curves hypotheses on EU countries. Sustainability. 2018;10(9):3327.

Zhang B, Wang B, Wang Z. Role of renewable energy and non-renewable energy consumption on EKC: evidence from Pakistan. J Clean Prod. 2017;156:855–64.

Lapinskienė G, Tvaronavičienė M, Vaitkus P. Greenhouse gases emissions and economic growth–evidence substantiating the presence of environmental Kuznets curve in the EU. Technol Econ Dev Econ. 2014;20(1):65–78.

Özokcu S, Özdemir Ö. Economic growth, energy, and environmental Kuznets curve. Renew Sustain Energy Rev. 2017;72:639–47.

Akbostancı E, Türüt-Aşık S, Tunç Gİ. The relationship between income and environment in Turkey: is there an environmental Kuznets curve? Energy Policy. 2009;37(3):861–7.

Nasreen S, Mbarek MB, Atiq-ur-Rehman M. Long-run causal relationship between economic growth, transport energy consumption and environmental quality in Asian countries: evidence from heterogeneous panel methods. Energy. 2020;192: 116628.

Poon JP, Casas I, He C. The impact of energy, transport, and trade on air pollution in China. Eurasian Geogr Econ. 2006;47(5):568–84.

Gjorgievski VZ, Cundeva S, Georghiou GE. Social arrangements, technical designs and impacts of energy communities: a review. Renew Energy. 2021;169:1138–56.

Alimujiang A, Jiang P. Synergy and co-benefits of reducing CO2 and air pollutant emissions by promoting electric vehicles—a case of Shanghai. Energy Sustain Dev. 2020;55:181–9.

Zandalinas SI, Fritschi FB, Mittler R. Global warming, climate change, and environmental pollution: recipe for a multifactorial stress combination disaster. Trends Plant Sci. 2021;26(6):588–99.

Lu Q, et al. Potential energy conservation and CO2 emissions reduction related to China’s road transportation. J Clean Prod. 2020;245: 118892.

Mittal S, Dai H, Shukla P. Low carbon urban transport scenarios for China and India: a comparative assessment. Transp Res Part D Transp Environ. 2016;44:266–76.

Wang SS, et al. CO2 emissions, energy consumption and economic growth in China: a panel data analysis. Energy Policy. 2011;39(9):4870–5.

Arroyo M, Miguel LJ. The trends of the energy intensity and CO2 emissions related to final energy consumption in Ecuador: scenarios of national and worldwide strategies. Sustainability. 2019;12(1):20.

Roinioti A, Koroneos C. The decomposition of CO2 emissions from energy use in Greece before and during the economic crisis and their decoupling from economic growth. Renew Sustain Energy Rev. 2017;76:448–59.

Khan SAR, et al. Green supply chain management, economic growth and environment: a GMM based evidence. J Clean Prod. 2018;185:588–99.

Zhao F, et al. The correlated impacts of fuel consumption improvements and vehicle electrification on vehicle greenhouse gas emissions in China. J Clean Prod. 2019;207:702–16.

Alataş S. Do environmental technologies help to reduce transport sector CO2 emissions? Evidence from the EU15 countries. Res Transp Econ. 2022;91: 101047.

Alshehry AS, Belloumi M. Study of the environmental Kuznets curve for transport carbon dioxide emissions in Saudi Arabia. Renew Sustain Energy Rev. 2017;75:1339–47.

Erdogan S, et al. Testing the transport-induced environmental Kuznets curve hypothesis: the role of air and railway transport. J Air Transp Manage. 2020;89: 101935.

Gyamfi BA, et al. Beyond the environmental Kuznets curve: do combined impacts of air transport and rail transport matter for environmental sustainability amidst energy use in E7 economies? Environ Dev Sustain. 2022. https://doi.org/10.1007/s10668-021-01944-6 .

Fujii H, et al. An analysis of urban environmental Kuznets curve of CO2 emissions: empirical analysis of 276 global metropolitan areas. Appl Energy. 2018;228:1561–8.

Sarkodie SA, Strezov V. A review on environmental Kuznets curve hypothesis using bibliometric and meta-analysis. Sci Total Environ. 2019;649:128–45.

Nathaniel S, et al. Energy consumption, FDI, and urbanization linkage in coastal Mediterranean countries: re-assessing the pollution haven hypothesis. Environ Sci Pollut Res. 2020;27:35474–87.

Nathaniel SP, Adeleye N. Environmental preservation amidst carbon emissions, energy consumption, and urbanization in selected African countries: implication for sustainability. J Clean Prod. 2021;285: 125409.

Paramati SR, Shahzad U, Doğan B. The role of environmental technology for energy demand and energy efficiency: evidence from OECD countries. Renew Sustain Energy Rev. 2022;153: 111735.

Onafowora OA, Owoye O. Bounds testing approach to analysis of the environment Kuznets curve hypothesis. Energy economics. 2014;44:47–62.

Dijkgraaf E, Vollebergh HR. A test for parameter homogeneity in CO 2 panel EKC estimations. Environ Resource Econ. 2005;32:229–39.

Pasaran S, Shine Y, Smith R. Bound testing approach to the analysis of level relationship. J Appl, 2001.

Bölük G, Mert M. The renewable energy, growth and environmental Kuznets curve in Turkey: an ARDL approach. Renew Sustain Energy Rev. 2015;52:587–95.

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OJO: Conceptualization, methodology, software application, validation, formal analysis, investigation, data resources, data curation, writing—original draft preparation, writing—review and editing, OJO, OAO and CKM Lee: visualization, project administration. OAO and CKM Lee: supervision. OAO and CKM Lee: internal funding acquisition.

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Oladunni, O.J., Olanrewaju, O.A. & Lee, C.K.M. The Environmental Kuznets Curve (EKC) Hypothesis on GHG emissions: analyses for transportation industry of South Africa. Discov Sustain 5 , 302 (2024). https://doi.org/10.1007/s43621-024-00518-6

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