context free in research

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Educational Research Basics by Del Siegle

Qualitative research paradigm.

I am amazed how often we hear qualitative researchers applying their standards to quantitative research or quantitative researchers applying their standards to qualitative research. Each functions within different assumptions. Finding fault with one approach with the standards of another does little to promote understanding. Each approach should be judges on its theoretical basis.

The Assumptions of Qualitative Designs

• Qualitative researchers are concerned primarily with process , rather than outcomes or products.
• Qualitative researchers are interested in meaning: ­how people make sense of their lives, experiences, and their structures of the world.
• The qualitative researcher is the primary instrument for data collection and analysis. Data are mediated through this human instrument, rather than through inventories, questionnaires, or machines.
• Qualitative research involves fieldwork . The researcher physically goes to the people, setting, site, or institution to observe or record behavior in its natural setting.
• Qualitative research is descriptive in that the researcher is interested in process, meaning, and understanding gained through words or pictures.
• The process of qualitative research is inductive in that the researcher builds abstractions, concepts, hypotheses, and theories from details.

…..Merriam, S. B. (1988). Case study research in education: A qualitative approach. San Francisco, CA: Jossey-Bass.

….. Creswell, J. W. (1994). Research design: Qualitative & quantitative approaches . Thousand Oaks, CA: Sage Publications.

Arguments Supporting Qualitative Inquiry

• Human behavior is significantly influenced by the setting in which it occurs; thus one must study that behavior in situations. The physical setting (­e.g., schedules, space, pay, and rewards­) and the internalized notions of norms, traditions, roles, and values are crucial contextual variables. Research must be conducted in the setting where all the contextual variables are operating.
• Past researchers have not been able to derive meaning…from experimental research.
• The research techniques themselves, in experimental research, [can]…affect the findings. The lab, the questionnaire, and so on, [can]…become artifacts. Subjects [can become]…either suspicious and wary, or they [can become]…aware of what the researchers want and try to please them. Additionally, subjects sometimes do not know their feelings, interactions, and behaviors, so they cannot articulate them to respond to a questionnaire.
• One cannot understand human behavior without understanding the framework within which subjects interpret their thoughts, feelings, and actions. Researchers need to understand the framework. In fact, the “objective ” scientist, by coding and standardizing, may destroy valuable data while imposing her world on the subjects.
• Field study research can explore the processes and meanings of events.

…..Marshall, C., & Rossman, G. (1980). Designing qualitative research . Newbury Park, CA: Sage.

Predispositions of Quantitative and Qualitative Modes of Inquiry

Although some social science researchers (Lincoln & Guba, 1985; Schwandt, 1989) perceive qualitative and quantitative approaches as incompatible, others (Patton, 1990; Reichardt & Cook, 1979) believe that the skilled researcher can successfully combine approaches. The argument usually becomes muddled because one party argues from the underlying philosophical nature of each paradigm, and the other focuses on the apparent compatibility of the research methods, enjoying the rewards of both numbers and words. Because the positivist and the interpretivist paradigms rest on different assumptions about the nature of the world, they require different instruments and procedures to find the type of data desired. This does not mean, however, that the positivist never uses interviews nor that the interpretivist never uses a survey. They may, but such methods are supplementary, not dominant….Different approaches allow us to know and understand different things about the world….Nonetheless, people tend to adhere to the methodology that is most consonant with their socialized worldview. (p. 9)

….. Glesne, C., & Peshkin, A. (1992). Becoming qualitative researchers: An introduction. White Plains, NY: Longman.

Contrasting Positivist and Naturalist Axioms (Beliefs and Assumptions)

….. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry . Newbury Park, CA: Sage Publications.

….. Spradley, J. P. (1979). The ethnographic interview. Fort Worth, TX: Harcourt Brace Jovanovich College Publishers.

Five popular types of Qualitative Research are

• Ethnography
• Phenomenological
• Grounded Theory

Del Siegle, Ph.D [email protected] www.delsiegle.info

Contextualizing Your Research Project

• First Online: 23 October 2020

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In research, contextualization is a way of approaching our research project, or linking it to the relevant research and to the setting of the study. Contextualization gives credibility and support to our research project as a whole. Research contextualizing takes various shapes and forms. The two main ways in which research is contextualized will be illustrated and discussed in this chapter. First, we contextualize our research in relation to the established literature and prior studies. Second, we contextualize our research by linking it to (a) the specific context in which it was conducted like institution and workplace (micro-level); and (b) the location or general setting of the study like geographic territory and location (macro-level). Based on these two main ways of contextualizing research, a further two-fold wider contextualization of our research project will be identified too, namely, linking our research to other disciplines (domains of study) and other contexts (locations). In all cases, our research must be contextualized and grounded in theory and/or practice. This chapter, consolidated by abundant illustrative examples, takes the reader along these paths of contextualizing research.

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Shehadeh, A. (2020). Contextualizing Your Research Project. In: Coombe, C., Anderson, N.J., Stephenson, L. (eds) Professionalizing Your English Language Teaching. Second Language Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-34762-8_27

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Home » Context of the Study – Writing Guide and Examples

Context of the Study – Writing Guide and Examples

Table of Contents

Context of the Study

The context of a study refers to the set of circumstances or background factors that provide a framework for understanding the research question , the methods used, and the findings . It includes the social, cultural, economic, political, and historical factors that shape the study’s purpose and significance, as well as the specific setting in which the research is conducted. The context of a study is important because it helps to clarify the meaning and relevance of the research, and can provide insight into the ways in which the findings might be applied in practice.

Structure of Context of the Study

The structure of the context of the study generally includes several key components that provide the necessary background and framework for the research being conducted. These components typically include:

• Introduction : This section provides an overview of the research problem , the purpose of the study, and the research questions or hypotheses being tested.
• Background and Significance : This section discusses the historical, theoretical, and practical background of the research problem, highlighting why the study is important and relevant to the field.
• Literature Review: This section provides a comprehensive review of the existing literature related to the research problem, highlighting the strengths and weaknesses of previous studies and identifying gaps in the literature.
• Theoretical Framework : This section outlines the theoretical perspective or perspectives that will guide the research and explains how they relate to the research questions or hypotheses.
• Research Design and Methods: This section provides a detailed description of the research design and methods, including the research approach, sampling strategy, data collection methods, and data analysis procedures.
• Ethical Considerations : This section discusses the ethical considerations involved in conducting the research, including the protection of human subjects, informed consent, confidentiality, and potential conflicts of interest.
• Limitations and Delimitations: This section discusses the potential limitations of the study, including any constraints on the research design or methods, as well as the delimitations, or boundaries, of the study.
• Contribution to the Field: This section explains how the study will contribute to the field, highlighting the potential implications and applications of the research findings.

How to Write Context of the study

Here are some steps to write the context of the study:

• Identify the research problem: Start by clearly defining the research problem or question you are investigating. This should be a concise statement that highlights the gap in knowledge or understanding that your research seeks to address.
• Provide background information : Once you have identified the research problem, provide some background information that will help the reader understand the context of the study. This might include a brief history of the topic, relevant statistics or data, or previous research on the subject.
• Explain the significance: Next, explain why the research is significant. This could be because it addresses an important problem or because it contributes to a theoretical or practical understanding of the topic.
• Outline the research objectives : State the specific objectives of the study. This helps to focus the research and provides a clear direction for the study.
• Identify the research approach: Finally, identify the research approach or methodology you will be using. This might include a description of the data collection methods, sample size, or data analysis techniques.

Example of Context of the Study

Here is an example of a context of a study:

Title of the Study: “The Effectiveness of Online Learning in Higher Education”

The COVID-19 pandemic has forced many educational institutions to adopt online learning as an alternative to traditional in-person teaching. This study is conducted in the context of the ongoing shift towards online learning in higher education. The study aims to investigate the effectiveness of online learning in terms of student learning outcomes and satisfaction compared to traditional in-person teaching. The study also explores the challenges and opportunities of online learning in higher education, especially in the current pandemic situation. This research is conducted in the United States and involves a sample of undergraduate students enrolled in various universities offering online and in-person courses. The study findings are expected to contribute to the ongoing discussion on the future of higher education and the role of online learning in the post-pandemic era.

Context of the Study in Thesis

The context of the study in a thesis refers to the background, circumstances, and conditions that surround the research problem or topic being investigated. It provides an overview of the broader context within which the study is situated, including the historical, social, economic, and cultural factors that may have influenced the research question or topic.

Context of the Study Example in Thesis

Here is an example of the context of a study in a thesis:

Context of the Study:

The rapid growth of the internet and the increasing popularity of social media have revolutionized the way people communicate, connect, and share information. With the widespread use of social media, there has been a rise in cyberbullying, which is a form of aggression that occurs online. Cyberbullying can have severe consequences for victims, such as depression, anxiety, and even suicide. Thus, there is a need for research that explores the factors that contribute to cyberbullying and the strategies that can be used to prevent or reduce it.

This study aims to investigate the relationship between social media use and cyberbullying among adolescents in the United States. Specifically, the study will examine the following research questions:

• What is the prevalence of cyberbullying among adolescents who use social media?
• What are the factors that contribute to cyberbullying among adolescents who use social media?
• What are the strategies that can be used to prevent or reduce cyberbullying among adolescents who use social media?

The study is significant because it will provide valuable insights into the relationship between social media use and cyberbullying, which can be used to inform policies and programs aimed at preventing or reducing cyberbullying among adolescents. The study will use a mixed-methods approach, including both quantitative and qualitative data collection and analysis, to provide a comprehensive understanding of the phenomenon of cyberbullying among adolescents who use social media.

Context of the Study in Research Paper

The context of the study in a research paper refers to the background information that provides a framework for understanding the research problem and its significance. It includes a description of the setting, the research question, the objectives of the study, and the scope of the research.

Context of the Study Example in Research Paper

An example of the context of the study in a research paper might be:

The global pandemic caused by COVID-19 has had a significant impact on the mental health of individuals worldwide. As a result, there has been a growing interest in identifying effective interventions to mitigate the negative effects of the pandemic on mental health. In this study, we aim to explore the impact of a mindfulness-based intervention on the mental health of individuals who have experienced increased stress and anxiety due to the pandemic.

Context of the Study In Research Proposal

The context of a study in a research proposal provides the background and rationale for the proposed research, highlighting the gap or problem that the study aims to address. It also explains why the research is important and relevant to the field of study.

Context of the Study Example In Research Proposal

Here is an example of a context section in a research proposal:

The rise of social media has revolutionized the way people communicate and share information online. As a result, businesses have increasingly turned to social media platforms to promote their products and services, build brand awareness, and engage with customers. However, there is limited research on the effectiveness of social media marketing strategies and the factors that contribute to their success. This research aims to fill this gap by exploring the impact of social media marketing on consumer behavior and identifying the key factors that influence its effectiveness.

Purpose of Context of the Study

The purpose of providing context for a study is to help readers understand the background, scope, and significance of the research being conducted. By contextualizing the study, researchers can provide a clear and concise explanation of the research problem, the research question or hypothesis, and the research design and methodology.

The context of the study includes information about the historical, social, cultural, economic, and political factors that may have influenced the research topic or problem. This information can help readers understand why the research is important, what gaps in knowledge the study seeks to address, and what impact the research may have in the field or in society.

Advantages of Context of the Study

Some advantages of considering the context of a study include:

• Increased validity: Considering the context can help ensure that the study is relevant to the population being studied and that the findings are more representative of the real world. This can increase the validity of the study and help ensure that its conclusions are accurate.
• Enhanced understanding: By examining the context of the study, researchers can gain a deeper understanding of the factors that influence the phenomenon under investigation. This can lead to more nuanced findings and a richer understanding of the topic.
• Improved generalizability: Contextualizing the study can help ensure that the findings are applicable to other settings and populations beyond the specific sample studied. This can improve the generalizability of the study and increase its impact.
• Better interpretation of results: Understanding the context of the study can help researchers interpret their results more accurately and avoid drawing incorrect conclusions. This can help ensure that the study contributes to the body of knowledge in the field and has practical applications.

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The 3 Cs of Content, Context, and Concepts: A Practical Approach to Recording Unstructured Field Observations

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Most primary care researchers lack a practical approach for including field observations in their studies, even though observations can offer important qualitative insights and provide a mechanism for documenting behaviors, events, and unexpected occurrences. We present an overview of unstructured field observations as a qualitative research method for analyzing material surroundings and social interactions. We then detail a practical approach to collecting and recording observational data through a “3 Cs” template of content, context, and concepts. To demonstrate how this method works in practice, we provide an example of a completed template and discuss the analytical approach used during a study on informed consent for research participation in the primary care setting of Qatar.

• primary care
• family medicine
• general medicine
• research methodology
• qualitative research
• mixed methods: participatory/action research
• field observations
• field notes
• Hawthorne effect
• communication
• behavioral research
• informed consent
• INTRODUCTION

Clinical observation is a method well known to primary care physicians. It is the rare physician who takes a patient’s words at face value without also using contextual clues, such as the patient’s appearance and behavior, to construct a picture of the patient’s health. 1 Indeed, medical education has recently highlighted the need for physicians to be more observant through innovative curricula that teach observational skills by examining art. 2 , 3

Field observations offer insight into behaviors and the environment 4 and can play an important role in primary care research. A decades-long history of observational research in primary care has demonstrated how contextual factors both in and out of the clinic influence the effectiveness of interventions, as well as clinical outcomes. 5 – 9 A recent commentary in JAMA Internal Medicine has called for more “ethnographic and field studies” to capitalize on the “value of direct observation,” particularly in studies of patient safety. 10 (p1024)

Unfortunately, most primary care researchers lack a practical approach for including field observations in their studies. Here, we present an overview of unstructured field observations as a qualitative research method for analyzing material surroundings and social interactions, aimed at researchers new to unstructured observations. We then detail a practical approach to collecting and recording observational data through a “3 Cs” template of content, context, and concepts. To demonstrate how this method works in practice, we provide an example of a completed template and discuss the analytical approach used during a study on informed consent for research participation in the primary care setting of Qatar.

Observational field research has its roots in the social sciences and is most often associated with participant observation fieldwork in cultural anthropology and sociology, though it has spread to disciplines as diverse as nursing, education, and social work. 11 Although he was not the first to undertake fieldwork, the Polish anthropologist Bronislaw Malinowski has been ascribed with popularizing participant observation as a research method. 12 Unlike structured interview sessions, participant observation allows for more flexible interactions with research participants through social events, casual conversations, and semi-structured interviews conducted in settings that are a natural part of participants’ everyday lives. 13 There is some debate as to how much a researcher should “participate” in daily activities, 12 but it is often enough simply “to be tolerated as an unobtrusive observer.” 14 (p195)

Malinowski’s work was influenced by the scientific positivism of the era, but later social scientists launched an antipositivist (interpretivist) critique that shifted the focus of observational research from a search for social facts to an understanding of cultural meanings. 15 We follow an interpretivist approach in believing the purpose of observational research is to arrive at what the anthropologist Clifford Geertz called “thick description.” 16 In a famous example, Geertz explains how the same action (a quick blink of the right eye) holds different meanings based on the cultural context in which it occurs. 16 Only in a specific context will a blink be recognized as a wink—a conspiratorial signal to a friend—rather than as a meaningless twitch of the eye. Thick description, then, uses empirical data from multiple sources to contextualize individual behavior and interpret its meaning.

A crucial assumption in participant observation is that the researcher is the instrument of data collection. 17 This assumption means all data are filtered through the researcher, through his or her personal characteristics, background, and experiences. 18 The result is a different relationship to objectivity than that found in the natural sciences. Observational research does not seek objectivity through a conceptual separation between researcher and study participants and phenomena. 19 Researchers are not blank slates; instead, they use prior knowledge and experiences as either implicit or explicit bases of comparison to understand what they observe in the field. 20 Personal bias is not seen as a flaw but as part of data collection. 12 Qualitative researchers speak of using bias to explore hunches or ideas, as well as to seek out information contrary to their own views. 21 It is critical before going into the field for researchers to conduct a personal inventory and reflect on their preconceptions, as these will affect their interpretation of events. 21

Researchers must also remain aware of potential differences in status between themselves and their study participants, where higher status may be conferred by socioeconomic, educational, occupational, or other types of privilege, or some combination thereof. 19 Such differences create a certain power dynamic between researchers and participants that can influence data collection. 19 A reflective activity, such as journaling or freewriting, can help define the researcher’s lens and help the researcher recognize how he or she may be perceived in the field. 22

• RATIONALE FOR COLLECTING FIELD OBSERVATIONS

Perhaps the most compelling reason for conducting observations is to understand behavior ( Table 1 ). Studies have suggested that nonverbal behavior plays a considerable role in communicating shared cultural meanings. 23 Furthermore, emotions are communicated more through facial expression and vocal tone and inflection than through spoken word. 24 , 25 Finally, as it pertains to health research, individuals tend to underreport their unhealthy or socially undesirable behaviors 26 , 27 and overreport the converse. 28 Observational research thus contributes to understanding human behavior in all of these scenarios.

• View inline

Reasons for Conducting Observations in Primary Care Research, With Clinical Examples

Field observations can help researchers understand how the interactions and activities in a given setting inform behaviors and beliefs ( Table 1 ). 29 They contribute to uncovering the broader context of a given scenario, making observational research especially well suited for studying process. 19 In primary care, for example, observational research has deepened understanding of the clinical contexts in which practice transformation to patient-centered medical homes has occurred. 30 – 36 Field observations have also helped contextualize the (quantitative) results of randomized controlled trials, by providing insight into why certain randomized controlled trials have succeeded or failed. 9 , 37

Thus, field observations allow researchers to move beyond selected perceptions and protocols, and better understand the nature of particular activities, who performs them, and the contexts in which they occur.

Observational research may not always be appropriate, and under certain circumstances, participants will not tolerate an observer ( Table 2 ). Individuals who engage in socially undesirable or illegal behavior may be reluctant to have an observer present or be at risk personally 12 (although the opposite might also be true 38 ). Sometimes the presence of an observer might change participants’ behavior, a phenomenon known as the Hawthorne effect. 39 Research has shown this effect may not be as great as feared, 40 however, and multiple observations over the long term can be a mitigating factor. 29

Potential Reasons for Not Conducting Unstructured Observations in Primary Care Research, With Clinical Examples

• UNSTRUCTURED AND STRUCTURED FIELD OBSERVATIONS

A central tenet of observational research is that observations must be recorded to count as data, 12 and it is only through the regular and systematic recording of field observations that researchers create texts for subsequent analysis. 41 Field observations can be structured, unstructured, or a combination of both. Structured observations use a template to record tabulations of specific behaviors that can be measured and analyzed statistically. 42 These observations often focus on highly specific issues and are indicated when the intent is to validly and reliably measure specific behaviors. Unstructured observational data use the researcher’s words for thick description of phenomena or events. These words emerge through the researcher’s experience in the field. 43 Even though unstructured, the observations are still focused because they address a research question or specific phenomenon. Within that focus, there is great latitude in what the researcher can choose to record ( Table 3 ).

The 3 Cs—Context, Content, and Concepts—Approach to Field Observations

To illustrate the potential of field observations, we introduce a study that relied heavily on observational data supplemented by interviews. The first author (M.D.F.) served as co–principal investigator on a multistage, mixed methods parent study in Qatar to develop a health care quality assessment instrument that was adapted for populations whose native languages were Arabic, English, Hindi, and Urdu. 44 A theoretical model, Cultural Construction of Clinical Reality developed by Kleinman et al, 45 guided the research. During the first stage of qualitative data collection, Qatari team members expressed concern about how potential participants would respond to recruitment strategies dictated by US–based institutional review boards. Existing literature had little guidance for recruiting participants in Arabian Gulf countries. We therefore conducted a study to explore how individuals in Qatar responded to in-person recruitment requests. 46 The 3 Cs template emerged from the need for institutional review board approval of data collection instruments and as a training instrument for research assistants.

• THE 3 CS UNSTRUCTURED FIELD OBSERVATIONS TEMPLATE

The 3 Cs template provides an easy approach to collecting observational data. A full 3 Cs template (Supplemental Appendix 1, available at http://www.AnnFamMed.org/content/17/6/554/suppl/DC1/ ) includes the study metadata (details about the project), the research question, and the 3 Cs of context, content, and concepts.

Field Observation Metadata

The study metadata include information such as the project title, the study document type, the observer, the date and time, the location, and a participant description (eg, ID, descriptor if multiple individuals). The metadata from a single observation collected during the Qatar recruitment study are given in Supplemental Appendix 2, available at http://www.AnnFamMed.org/content/17/6/554/suppl/DC1/ . Additional information here included the language spoken by the researcher and participant.

Research Question

The research question keeps observations focused on the purpose of the study. Because there is bound to be individual variation in data collection (recalling that the researcher is the instrument of data collection), including the question on the template provides a focal point for recording details. As illustrated in Supplemental Appendix 2, there were both primary and secondary research questions for researchers to address.

Context, Content, and Concepts

The context, content, and concepts are summarized in Table 3 and explained in further detail below.

The first C, context, has a dual meaning: its most immediate referent is the circumstances in which the researcher is making observations (eg, who is doing the observation, where, and when). In a broader sense, context also refers to the researcher’s (or team’s) prior research experience, and could include knowledge gained from reading the scholarly literature, and additional information about a population, organization, or community—anything that speaks to the social surroundings and recent events that may influence the present interaction. 47 Context can act as a reminder of the researcher’s purpose in using observational methods—and may also include information about the researcher’s introduction to the field (eg, “invited by Dr A to observe her clinic” or “clinic recruited through practice manager”). Visuals, including sketches or photographs (if possible), may be useful, especially if certain structural features stand out (eg, small vs large waiting room; front staff behind an open desk vs behind a glass partition). This information can enable quick and easy comparisons between multiple locations.

The second C, content, is more focused than context, as it refers directly to what happens during the observation period. (We recommend that researchers record each observation period on a separate template, creating multiple shorter documents, for each episode of observing, rather than in a single long document, for later analysis.) Although observational research is often touted as holistic—that is, covering everything within the 5 senses during a given stretch of time—in truth, researchers continually make decisions about where to point their focus. The research question and the project’s theoretical orientation (the research questions, and the first C, context, described previously) are paramount here in guiding the observations.

The third C, concepts, is a space for the researcher to connect the minutiae of his or her field observations with the bigger picture, to think back to the research question or hypothesis and compare theory with practice. It can be used to reflect about the process of research, or procedural or ethical questions that arise in the field. It is a space for nascent analysis, where the researcher can try out new ideas based on insights gained in the field. Researchers may take note of emergent patterns or themes (as in grounded theory 48 ), may reflect on their experience in the field, or both. In this way, the third C provides a sort of running commentary on the field observations, which serves both as the foundation for later analysis and as an audit trail that demonstrates how observation, data collection, theory, and analysis are all intertwined. 15

• USING THE 3 CS APPROACH IN THE FIELD

Recording Field Jottings

There is never enough time in the field to write down everything one observes. Indeed, recording observations becomes even more difficult when the researcher participates in ongoing activities by, for example, engaging in conversation, directing a lost patient to the exit, or lending a hand to set up for a meeting. Furthermore, most of the time, a field researcher does not bring a computer into the field but rather relies on pen and paper, the latter being less obtrusive and easier to manage (although that has changed with the advent of smartphones and tablets). Generally speaking, therefore, no one writes actual field notes in the field. Rather, they capture field jottings—words, phrases, or drawings to jog their memories after the fact. 41 In the Qatar recruitment study, 46 the research assistants used a printed 3 Cs template for jotting down ideas as they occurred.

Expanding on Field Jottings

After leaving the field site, the observer has the opportunity to construct, from field jottings, a more extensive record of what was observed. 29 It is important to write these expanded field notes as soon as possible after the observation session (hours, not days, later); the more time that passes, the greater chance that the researcher will recall fewer details. A first pass should focus on writing descriptive text focusing on the “what” and “how” while avoiding summative or generalizing statements. The best field notes are rich with details that convey a sense of what it is like to be in the time and space described (ie, thick description). Expanded and edited notes can be found in Supplemental Appendix 2.

Working With a Research Team and Analyzing the Data

Primary care research is typically team based. The 3 Cs approach to field observations can facilitate iterative data collection and analysis. Field notes should be shared among team members both to encourage the entire team’s familiarity with the field and to identify salient points for future data collection and pattern recognition. The first few observational sessions should be the most detailed, as this is when the researcher is least familiar with the field. 15 Of course, not all details will ultimately prove relevant, but they can act as a starting point for more refined observations later on. Furthermore, after the researcher begins to recognize what counts as the norm in a given setting, he or she can then more easily identify deviations from that norm. Such surprises in the field can lead to unexpected insights that generate new directions for future research.

Field notes can be analyzed like any other qualitative text by, for example, searching for key words and coding for thematic content. Even if they are not formally analyzed, field notes may provide important context for understanding data derived from (time-limited) interviews and surveys. In the Qatar recruitment study, 46 the team conducted iterative analysis of the textual data from field observations and interviews, which led to the creation of a coding scheme in Atlas. ti (ATLAS.ti Scientific Software Development GmbH). The team also integrated numerical data from the demographic instrument and recruitment procedures, to create a narrative format for the results.

Finishing Field Observations in a Study

A final consideration is when to end the observational period or series. One possibility is to have a predetermined end point. In the Qatar recruitment study, 46 field observations continued until the study recruitment goals were met. An alternative is to seek data saturation. Guidelines for determining the saturation point in qualitative research vary, although a recent review calls for saturation to be operationalized in terms of research question(s), theoretical framework, and analytical goals. 49

Field observations are paramount in understanding contextual factors in any research project and have the potential to reveal important insights about the way actors and institutions work in a given place and time. Observational research is inductive and iterative, 15 and its greatest strength is its open-endedness. Because the researcher does not work in a controlled environment or with a standardized checklist or questionnaire, he or she is able to capture any data that do not fit into a priori categories. In such a setting, the research question serves as a guide, not a mandate, and it leaves room to address unexpected occurrences.

Although the 3 Cs template provides an excellent entrée for the use of field observations, there are other observational alternatives. Structured observations may be better indicated for inquiry exploring systematically the nature and metrics of phenomena, with integration across multiple observers for the purpose of statistical analysis. Another option is video recording, which yields multiple data sources, such as verbal, paraverbal, and nonverbal responses, and requires specific levels of skills and techniques for analysis. 50

The 3 Cs approach to unstructured field observations can be used when observation is the primary research method 46 or in tandem with another research method, such as qualitative interviews. 51 In the Qatar recruitment study, the observations were conducted more with the intent of being supplemental, but ultimately served as the primary source for a specific publication. 46 The template, which can be submitted for institutional review board approval, provides a straightforward mechanism for recording events and behaviors in almost any project involving human participants. As mixed methods gain increasing popularity in health services research, 52 unstructured field observations can play an important role in contextualizing other types of qualitative and quantitative data, resulting in more rigorous research designs and data collection.

Conflicts of interest: authors report none.

To read or post commentaries in response to this article, see it online at http://www.AnnFamMed.org/content/17/6/554 .

Previous presentations: Portions of the content of this article were presented at the 2012 North American Primary Care Research Group (NAPCRG) Annual Meeting; December 1-5, 2012; New Orleans, Louisiana; and at the 2013 NAPCRG Annual Meeting; November 9-13, 2013; Ottawa, Canada.

Supplemental Materials: Available at http://www.AnnFamMed.org/content/17/6/554/suppl/DC1/ .

• Received for publication November 4, 2018.
• Revision received April 12, 2019.
• Accepted for publication May 7, 2019.
• © 2019 Annals of Family Medicine, Inc.
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Context Matters for Foundation Models in Biology

Just as words can have multiple meanings depending on the context of a sentence, proteins can play different roles in a cell based on their cellular environments. Advances in our understanding of protein and biomolecule functions have been propelled by recent breakthroughs in transformer-based models, such as large language models and generative pre-trained transformers, which automatically learn word semantics from diverse language contexts. Innovating a similar approach for protein functions—viewing them as distributions across various cellular contexts—could enhance the use of foundation models in biology. This would allow the models to dynamically adjust their outputs based on the biological contexts in which they operate. To this end, we have developed PINNACLE , a novel contextual AI model for single-cell biology that supports a broad array of biomedical AI tasks by tailoring its outputs to the cell type context in which the model is asked to make predictions. 

To glean the meaning of a word, we examine nearby words for context clues. For example, “buy an apple” and “grow an apple” yield different recommendations: the first phrase is used to refer to apple products, whereas the second is better associated with apple trees (Figure 1a). To resolve the role of a protein, we interrogate it in the context of the proteins with which it interacts and the cells in which it exists. For instance, H2AFX is a gene that can be involved in homologous recombination or end joining depending on its cellular context (Figure 1b).

Cellular context is critical to understanding protein function and developing molecular therapies. Still, modeling proteins across biological contexts, such as the cell types that they are activated in and the proteins they interact with, remains an algorithmic challenge. Current approaches are context-free: learning on a reference context-agnostic dataset, a single context at a time, or an integrated summary across multiple contexts. As a result, they cannot tailor outputs based on a given context, which can lead to poor predictive performance when applied to a context-specific setting or a never-before-seen context. We develop PINNACLE, a new geometric deep learning approach that generates context-aware protein representations to address these challenges.

Context-specific geometric deep learning PINNACLE model

PINNACLE is a novel geometric deep learning model that learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. By leveraging a multi-organ single-cell atlas Tabula Sapiens from CZ CELLxGENE Discover , we construct 156 cell type specific protein interaction networks that are maximally similar to the global reference protein interaction network while maintaining cell type specificity (left and middle panels of Figure 2). We additionally create a metagraph to capture the tissue hierarchy and cell type communication among the cell type specific protein interaction networks (right panel of Figure 2). There are four distinct edge types in the metagraph: cell type to cell type (i.e., cell type interaction), cell type to tissue, tissue to cell type (i.e., tissue membership of the cell type), and tissue to tissue (i.e., parent-child tissue relationship in a tissue ontology). This results in multi-scale networks representing protein, cell type, and tissue information in a unified data representation.

PINNACLE’s algorithm specifies graph neural message passing transformations on multi-scale protein interaction networks . It performs neural message passing with attention for each cell type specific protein interaction network (component 1 in Figure 3) and metagraph (component 2 in Figure 3), and aligns the protein and cell type embeddings using an attention bridge (component 3 in Figure 3). First, PINNACLE learns a trainable weight matrix, node embeddings, and attention weights for each cell type specific protein interaction network. They are optimized based on two protein-level tasks: link prediction (i.e., whether an edge exists between a pair of proteins) and cell type identity (i.e., which cell type a protein is activated in). Secondly, on the metagraph, PINNACLE learns edge type specific trainable weight matrices, node embeddings, and attention weights and aggregates the edge type specific node embeddings via another attention mechanism. These trainable parameters are optimized using edge type specific link prediction (i.e., whether a specific type of edge exists between a pair of nodes). Thirdly, PINNACLE learns attention weights to bridge protein and cell type embeddings. This attention bridge facilitates the propagation of neural messages from cell types and tissues to the cell type specific protein embeddings. It enables PINNACLE to generate a unified embedding space of proteins, cell types, and tissues. Further, the attention bridge enforces cellular and tissue organization of the latent protein space based on tissue hierarchy and cell type communication, enabling contextualization of protein representations.

Context-specific predictions

PINNACLE’s contextual representations can be adapted for diverse downstream tasks in which context specificity may play a significant role. Designing safe and effective molecular therapies is one such task that requires understanding the mechanisms of proteins across cell type contexts. We hypothesize that, in contrast to context-free protein representations, contextualized protein representations can enhance 3D structure-based protein representations for resolving immuno-oncological protein interactions (Figure 4) and facilitate the investigation of drugs’ effects across cell types (Figure 5).

Contextualizing 3D molecular structures of proteins using existing structure-based models is limited by the scarcity of structures captured in context-specific conformations. We show via demonstrative case studies that PINNACLE’s contextualized protein representations can improve structure-based predictions of binding (and non-binding) proteins (Figure 4a). For two immuno-oncological protein interactors, PD-1/PD-L1 and CTLA-4/B7-1, we generate embeddings of each protein using a state-of-the-art structure-based model, MaSIF. We aggregate these structure-based embeddings with a corresponding genomic-based context-free or contextualized protein embedding (i.e., from PINNACLE). By calculating a binding score (i.e., cosine similarity between a pair of protein embeddings), we find that contextualized embeddings enable better differentiation between binding and non-binding proteins (Figure 4b). This zero-shot analysis of contextualizing 3D structure-based representations exemplifies the potential of contextual learning to improve the modeling of molecular structures across biological contexts.

Nominating therapeutic targets with cell type resolution holds the promise of maximizing the efficacy and safety of a candidate drug. However, it is not possible with current models to systematically predict cell type specific therapeutic potential across all proteins and cell type contexts. By finetuning PINNACLE’s contextualized protein representations, we demonstrate that PINNACLE outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis (RA) and inflammatory bowel diseases (IBD). We can pinpoint cell type contexts with higher predictive capability than context-free models (Figure 5a). In collaboration with RA and IBD clinical experts, we find that the most predictive cell types are indeed relevant to RA and IBD. Further, examining predictions of individual proteins across cell types allows us to interrogate each candidate target’s therapeutic potential in each cell type context.

PINNACLE is a contextual AI model for representing proteins with cell type resolution. While we demonstrate PINNACLE’s capabilities through cell type specific protein interaction networks, the model can easily be re-trained on any protein network. Our study focuses on single-cell transcriptomic data of healthy individuals, but we expect that training disease-specific PINNACLE models can enable even more accurate predictions of candidate therapeutic targets across cell type contexts. PINNACLE’s ability to adjust its outputs based on the context in which it operates paves the way for large-scale context-specific predictions in biology.  

PINNACLE exemplifies the potential of contextual AI to mimic distinctly human behavior, operating within specific contexts. As humans, we naturally consider and utilize context without conscious effort in our daily interactions and decision-making processes. For instance, we adjust our language, tone, and actions based on the environment and the people we interact with. This inherent ability to dynamically adapt to varying contexts is a cornerstone of human intelligence. In contrast, many current biomedical AI models often lack this contextual adaptability. They tend to operate in a static manner, applying the same logic and patterns regardless of differing biological environments. This limitation can hinder their effectiveness and accuracy in biological systems where context plays a crucial role.

By integrating contextual awareness, models such as PINNACLE can transform biomedical AI. We envision that context-aware models will dynamically adjust their outputs based on the specific cellular environments they encounter, leading to more accurate and relevant predictions and insights. This advancement enhances the functionality of AI models in biology and brings them a step closer to emulating the nuanced and adaptable nature of human thought processes. 

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Why ‘context’ is important for research

Context is something we’ve been thinking a lot about at ScienceOpen recently. It comes from the Latin ‘ con ’ and ‘ texere ’ (to form ‘ contextus ’), which means ‘weave together’. The implications for science are fairly obvious: modern research is about weaving together different strands of information, thought, and data to place your results into the context of existing research. This is the reason why we have introductory and discussion sections at the intra-article level.

But what about context at a higher level?

Context can defined as: “ The circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood .” Simple follow on questions might be then, what is the context of a research article? How do we define that context? How do we build on that to do science more efficiently? The whole point for the existence of research articles is that they can be understood by as broad an audience as possible so that their re-use is maximised.

There are many things that impinge upon the context of research. Paywalls, secretive and exclusive peer review, lack of discovery, lack of inter-operability, lack of accessibility. The list is practically endless, and a general by-product of a failure for traditional scholarly publishing models to embrace a Web-based era.

While a lot of excellent new research platforms now feature slick discovery tools and features, we feel that this falls short of what is really needed for optimal research re-use in the digital age.

Discovery is the pathway to context. Context of an article is all about how research fits into increasingly complex domains, and using structured networks to decipher its value. With the power of the internet at our disposal, putting research in context should be of key importance in a world where there is ever more research being published that is impossible to manually filter.

Tracking the genealogy of research

Citations are perhaps what we might consider to be academic context. These form the structured networks or genealogies of an idea in their rawest sense. Through citations we gain a small amount of understanding into how research is being re-used by other researchers, and also the gateway to understanding what it is those citations are telling us.

At ScienceOpen, we show all articles and article records that cite a particular research article, and also provide links to similar articles on our platform. These are drawn at the moment from almost 12 million article records, so can potentially form huge networks of information.

In addition we show which articles are most similar based on keywords, and also which open access articles are citing a particular work. You can explore each of these in more depth, and begin to track research networks! So it’s like enhanced discovery, but with a smattering of cherries on top.

Generating context through engagement

One of the great things about context is that it is flexible and can be defined by user engagement. Take peer review for example. This is a way of adding context to a paper, by drawing on external expertise and perspective to enhance the content of a research article. Peer evaluation of this sort is crucial for defining the context of a paper, and should not be hidden away out of sight and use. As we use public post-publication peer review at ScienceOpen, the full discussion and process of research is transparent.

Other ways of generating simple context are through sharing and recommendations of articles. The more this is done, the more you can understand which articles are of wider interest.

Social context

The rise of altmetrics can be seen as the broadening how we think about context. Altmetrics are a pathway to understanding how articles have been discussed, mentioned or shared in online sources including mainstream news outlets, blogs, and a variety of social networks.

On every single article record (almost 12 million at the moment), we show Altmetric scores. You can also sort searches by Altmetric, which provides additional context for which articles are generating the most societal discussion online. This is great if you want to track social media trends in a particular field, and again is all about placing research objects into a broader context.

So these are just some of the ways in which we put research in context, and we do it on a massive scale. Let us know in the comments what you think ‘research in context’ is all about, and why you think it’s important!

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Understanding and evaluating context-specific research

Tosta, S. (2024). Understanding and evaluating context-specific research . In M. Tcherni-Buzzeo, & F. Pyrczak (Eds.), Evaluating research in academic journals: A practical guide to realistic evaluation (8 ed.). Routledge Press.

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What does "context-free" mean in the term "context-free grammar"?

Given the amount of material that tries to explain what a context-free grammar (CFG) is, I found it surprising that very few (in my sample, less than 1 in 20) give an explanation on why such grammars are called "context-free". And, to my mind, none succeeds in doing so.

My question is, why are context-free grammars called context-free? What is "the context"? I had an intuition that the context could be other language constructs surrounding the currently analyzed construct, but that seems not to be the case. Could anyone provide a precise explanation?

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• 4 look up "most vexing parse" for C++ that will teach you why context-freeness is handy –  ratchet freak Commented Aug 15, 2014 at 18:28
• 6 I thought I knew what a context-free grammar was until I just read some Googled definitions. Now I wish I had an etch-a-sketch and a soft blanky...maybe I'll just go outside... +1 for a good question. Looking forward to some intelligible answers! –  BrianH Commented Aug 15, 2014 at 18:29
• Your intuition is what I understand it to be, even if the formal definition of "other language constructs surrounding the currently analyzed construct" is suitably arcane. But I'm not sure enough to post that as an answer. –  Telastyn Commented Aug 15, 2014 at 18:39
• 1 See wikipages on Context-free grammar and Chomsky hierarchy . In practice programming language parsing has some context, often handled "outside" of "context-free" (LR or LL) parsing, e.g. by some symbol table, attributes, or environment –  Basile Starynkevitch Commented Aug 15, 2014 at 19:07
• 1 Here, have an xkcd reference: xkcd.com/1090 –  CaptainCodeman Commented Aug 16, 2014 at 12:09

6 Answers 6

It means all of its production rules have a single non-terminal on their left hand side.

For example, this grammar which recognizes strings of matched parentheses ("()", "()()", "(())()", ...) is context-free:

The left-hand side of every rule consists of a single non-terminal (in this case it's always S , but there could be more.)

Now consider this other grammar which recognizes strings of the form {a^n b^n c^n : n >= 1} (e.g. "abc", "aabbcc", "aaabbbccc"):

If the non-terminal B is preceded by the terminal/literal character c , you rewrite that term to WB but if it's preceded by b , you expand to bb instead. This is presumably what the context-sensitivity of context-sensitive grammars is alluding to.

A context-free language can be recognized a push-down automaton . Whereas a finite state machine makes use of no auxiliary storage, i.e. its decision is based only on its current state and input, a push-down automaton also has a stack at its disposal and can peek at the top of the stack for taking decisions.

To see that in action, you can parse nested parentheses by moving left to right and pushing a left parentheses onto a stack each time you encounter one, and popping each time you encounter a right parentheses. If you never end up trying to pop from an empty stack, and the stack's empty at the end of the string, the string is valid.

For a context-sensitive language, a PDA isn't enough. You'll need a linear-bounded automaton which is like a Turing Machine whose tape isn't unlimited (though the amount of tape available is proportional to the input). Note that that describes computers pretty well - we like to think of them as Turing Machines but in the real world you can't grab arbitrarily more RAM mid-program. If it's not obvious to you how an LBA is more powerful than a PDA, an LBA can emulate a PDA by using part of its tape as a stack, but it can also choose to use its tape in other ways.

(If you're wondering what a Finite State Machine can recognize, the answer is regular expressions. But not the regexes on steroids with capture groups and look-behind/look-ahead you see in program languages; I mean the ones you can build with operators like [abc] , | , * , + , and ? . You can see that abbbz matches regex ab*z just by keeping your current position in the string and regex, no stack required.)

• 16 Very nice explanation. Although, a Turing machine's tape does not need to be infinite, only unlimited. There can be a tape-factory at either end that, when the machine bumps into it, simply makes more tape. That way, at any point in time, it is finite. –  Mike Dunlavey Commented Aug 15, 2014 at 19:36
• 2 @MikeDunlavey Thanks for the clarification, fixed it. –  Doval Commented Aug 15, 2014 at 19:41
• 10 But the tape factory would need infinite tape making materials, or infinite tape making materials making materials, or ... [stack overflow] –  flamingpenguin Commented Aug 15, 2014 at 22:08
• 9 @Mehrdad: You can simulate any number of stacks using two stacks: keep all the stacks stacked on top of each other on one stack and when you need to access some stack further down pop the upper stacks off and push them onto the second stack. This proves that n>2 stacks are not more powerful than 2 stacks. Now, whether 2 stacks are more powerful than 1 stack, that I don't know. My intuition says no, but that might depend on exactly what the stack primitives are. –  Jörg W Mittag Commented Aug 16, 2014 at 8:58
• 11 @JörgWMittag: two stacks is as good as a tape. Hand-wavily: use one stack as the left hand side of the tape and the other stack as the right hand side, relative to your current position. So a 2-PDA is a Turing machine. For primitives you just need to be able to pop a value from one stack and push it on the other, which is how you move along your tape. –  Steve Jessop Commented Aug 16, 2014 at 13:07

The other answers are quite long, even if accurate and correct. This is the short version.

If you have a string of characters (terminals and nonterminals) and you wish to replace a nonterminal in the string, a context-free grammar allows you to do that regardless of the characters surrounding the nonterminal.

Consider the following rules (lowercase are terminals, uppercase are nonterminals):

In the first rule, you can replace an A regardless of what appears around it (context). In the second rule, you cannot replace A unless it is followed by B . While both nonterminals will be replaced in that case, the important point is that the nonterminals surrounding the A matter. One cannot replace BA with a , or B with a : only an A followed by a B because the order, the context of the nonterminals is important. This means the context of a nonterminal matters in the second rule, making it context-sensitive, while the first rule is context-free.

• 1 This is a really good explanation, though I'm not qualified to vouch for its accuracy or completeness. Is it all there is to it? –  rick Commented Aug 19, 2014 at 2:57
• 1 Computer grammars are part of the Chomsky hierarchy . That article is a good place to start. Also, this topic should be part of any baccalaureate program in computer science. At the very least, universities should teach regular and context-free grammars since those comprise the overwhelming majority of languages that we programmers are likely to encounter. –  user22815 Commented Aug 19, 2014 at 4:17
• @Snowman:Very crisp.It would be better if you say that "you can't derive to a from AB unless A is followed by B instead of saying "you can't replace A " which might not be possible because actually you're replacing AB isn't it? –  justin Commented Oct 20, 2015 at 10:41
• @justin correct. I updated my answer to be more clear on this. –  user22815 Commented Oct 20, 2015 at 16:06
• @Snowman:Do you mean to replace A or AB in the second rule(context-sensitive)?I think you're still trying to replace A as said from your answer. –  justin Commented Oct 26, 2015 at 6:34

To understand the distinction and the terminology better, it's a good idea to contrast a context-free language like a n b n with a context-sensitive one like a n b n c n . (Notation: a, b, and c are literals here and the exponent n means repeating the literal n times, n >0, say.) For instance, aabbc or aabbbcc is not in the latter language, whereas aabbcc is.

An acceptor for the context-free language a n b n can contract a pair of a and b regardless what's around it (i.e. regardless of the context in which ab appears) and it will function correctly, accepting only strings in the language and rejecting anything else, i.e. the grammar is S -> aSb | ab . Note that there are no terminals on the left side of the production(s) . (There are two production rules, but we're just writing them compactly.) The acceptor can basically make a local, context-free decision.

In contrast, you can't do something like that for the context-sensitive language a n b n c n , because for latter you must remember somehow the context you were in, i.e. how many contractions of ab you do to match them with contractions of bc. A grammar for the latter language is

Note that you have both terminals and non-terminals on the left in the last two rules. The terminals on the left are the context in which the non-terminals can be expanded.

Bootnote regarding "contract" vs. "expand" terminology etc.: although the formal grammars are [formally, hah] generative, the way they're actually implemented in parsers is actually reductionist, i.e. you contact everything to a non-terminal, basically applying the rules "in reverse", which is why even the first grammar given above is not practical in a program (it would give you the famous shift-reduce conflict because you can't decide which rule to apply), but the above two grammars suffice for illustrating the distinction between context-free and context-sensitive. The issue of ambiguity in context-free grammars is rather complicated, and not really the topic of this question so I'm not going to say more here, especially since it turns out that Wikipedia has a decent article on that . In contrast its articles on context-free and especially the one on context-sensitive language are !@#$@!#$ especially if you are new to the topic... I guess that's more on my TODO list.

The answers above give a pretty good definition of what it is. Let's see if I can put it in my own words, so that you will have 23 explanations instead of 20. The whole purpose of a grammar, any grammar, is to figure out if a particular sentence is a sentence in the given language. However, what we really use grammars and parsing for is to figure out what the sentence means. It's like the old diagramming of a sentence you may or may not have done back in English class back in school. A sentence is made of a subject part and a predicate part, a subject part has a noun and maybe some adjectives, a predicate part has a verb and perhaps an object noun, with some more adjectives, etc.

If there were a grammar for English (and I don't think there is, not in the computer science sense) then it would have rules of the following form, called productions.

You could then write a program and hand it any sentence, and the program could use the grammar to figure out which part of the sentence each word is, and what relation they have to each other.

If in every production, there is only one thing on the left side, then that means that whenever you see the right side in the sentence, you are allowed to substitute in the left side. For instance whenever you saw adjective noun, you could say "That's a SubjectPart" without paying any attention to anything outside of that phrase.

However, English (even the simplified description of English I gave above) is context-sensitive. "Adjective noun" isn't always a SubjectPart, it could be a NounPhrase in a PredicatePart. It depends on the context. Let's expand our pseudo-English grammar a bit:

You can only make an "adjective noun" into an ObjectNounPhrase if it comes right after a VerbPhrase.

Basically, if you have a production and you can apply it any time you want, no matter what surrounds it, it is context-free.

You can always tell if a grammar is context free easily. Just check if there is more than one symbol on the left side of the arrows.

Any language might be described by more than one grammar. If some grammar for a language is context-free, the language is context free. It can be proven for some languages that there is no context-free grammar possible. I suppose there might be a context-free grammar for the simplified pseudo-English subset I am describing above.

As for why it matters, it requires a simpler kind of program to parse a context-free grammar. As noted in the other answers, it doesn't require the full power of a Turing machine to parse a context-free grammar. A lookahead LR(1) parser (which is a kind of pushdown machine) for a particular context-free grammar can parse any sentence in that grammar in time and space linear to the length of the sentence. If the sentence is in the language, the parser will produce a structure tree identifying what each symbol in the sentence means (or at least what part it plays in the structure). If the sentence is not in the grammar, the parser will notice and stop on the first symbol which is impossible to reconcile with the grammar and preceding symbols (on the first "error").

What's even better is that there are programs which you can give a description of a grammar, and a list of instructions about what to do with each part (in a sense attaching a "meaning" to each production) and the program will write the parser for you. The program will parse the sentence, find the structure, and run your instructions on each part of the structure. This kind of program is called a parser-generator or compiler-compiler.

This kind of language analysis was invented for automatic analysis of natural language (such as English) but it turns out that this is most useful for analyzing computer languages. A language designer can write a grammar which captures his new language, then run it through the parser-generator to get a program which parses his language, and translates, interprets, compiles, executes, etc if he wants.

In fact, in most cases you can't really do this. For instance, balanced parentheses are a context-free language, but a language where it is required to declare all variables before you use them is context-sensitive. The parser is a part of the compiler, but additional logic is required to enforce these other requirements. What you then have to do is write a grammar which captures as much of your language as possible, run that through a parser-generator, then write code which enforces the rest of the requirements (symbol table handler, etc).

We don't generally use context-sensitive grammars because they are much more poorly supported. I don't know if there is an equivalent to an LR(k) parser-generator for context-sensitive languages. Yes, a Turing machine (or linear bound machine) can parse one, but I don't know if there is a general algorithm for turning a context-sensitive grammar into a program for a Turing machine, in the sense that an LR(1) generator makes parse tables for a pushdown machine. My guess is that the tables which underlie the parser would be exponentially larger. In any case, CS students (like myself, back in the day) are usually taught context-free grammars and LR(1) parser generators such as YACC.

There are two senses of the term "context-free", as I alluded to in this earlier reply What Is Meant By 'Context-Free' :

"algebraic": relating to the property that the left-hand sides of phrase structure rules contain only a single non-terminal and that the grammar can be collated into a fixed-point system,

"separable", or "context-free" in the more direct sense: that the question of whether a phrase structure rule applies at a given location should be independent of the context surrounding that location.

These two senses are equivalent. This equivalence is not a trivial result. Moreover, the equivalence applies in a setting much more broad than what is normally covered by the classical treatment of grammars. I'll lay out the basics of the equivalence result here.

That, in turn, will answer the question in a more substantial way than you may see otherwise.

Grammars Over Monoids First, a brief review: the response is issued in the general setting of grammars over monoids M. The usual senses of a grammar are for languages, where the monoid M = X* is the free monoid generated by a set X that is deemed the "alphabet" of the language, and for translations, where the monoid X* × Y* is for word pairs, drawn from an input alphabet X and output alphabet Y. But the term "grammar", and almost everything discussed relating to this concept, can be applied generically to monoids.

If Q denotes the set of non-terminals for the grammar, then they may be conceived of as a set of variables that are affixed to the underlying monoid M to yield the free extension M[Q]. Its words have the form mqm⋯qm, where the number of q ∈ Q in it is the "degree" of the word, and they are interspersed with words m ∈ M. Words in M[Q] of degree 0 are those drawn from M, itself, thereby making M a sub-monoid of M[Q]. Similarly, Q may be considered a subset of M[Q] consisting of all degree 1 words of the form 1q1, where 1 ∈ M is the monoid identity (i.e. empty word) and q ∈ Q.

A grammar over M consists of a finite set H ⊆ M[Q] × M[Q] and a distinguished "start configuration" S ∈ M[Q], which is normally (but unnecessarily) restricted to S ∈ Q. The set H generates a derivation relation → over M[Q] consisting of 0 step derivations α → α, where α ∈ M[Q], 1 step derivations λαρ → λβρ, where (α,β) ∈ H and n step derivations α_0 → α_n, for n > 1, from a set of 1-step derivations α_i → α_{i+1}, where 0 ≤ i < n.

The "derive set" [α] for each configuration α ∈ M[Q] is given by [α] = { m ∈ M: α → m }. The derive set [S] for the top-level configuration is the subset of M generated by the grammar.

Algebraic Versus Separable A grammar may be called "algebraic" if the phrase structure rules are restricted to have only a single non-terminal on the left-hand side, i.e. if H ⊆ Q × M[Q]. A grammar may be called "separable" if its derive sets satisfy the properties: [m] = {m}, for m ∈ M and [αβ] = [α][β], for α, β ∈ M[Q]. This entails the property that the derivations of α ∈ M[Q] should be independent of what context they are applied in. In particular if λ, ρ ∈ M describe left and right word contexts for α, then [λαρ] = {λ}[α]{ρ}.

More precisely, we can call a grammar "separable" if (1) m → β implies m = β, and (2) αβ → γ implies that there is a factoring γ = α'β' such that α → α' and β → β'.

It might be possible to combine these two conditions directly into a strict left-right context condition that (3) λαρ → γ (where λ, ρ ∈ M are respectively left and right word contexts) implies a factoring γ = λα'ρ, where α → α'. I haven't checked into it (yet): whether (3) is equivalent to (1) and (2) taken together. You might also need the condition [1] = {1}, where 1 ∈ M[Q] is the monoid identity inherited from 1 ∈ M.

There were two results alluded to in the previous reply.

Algebraic Grammars Are Separable First, the following property

if α → α' and β → β' then αβ → α'β'

is easy to establish by an inductive argument from the definition given for the "→" relation. This shows that [α][β] ⊆ [αβ]. This applies generically to all grammars over monoids.

To show the opposite inclusion, we take γ ∈ [αβ], i.e. αβ → γ. If the derivation is length 0, then γ = αβ ∈ [α][β]. If it is of length 1 or more, than it may be decomposed into αβ → λqρ → λδρ ∈ M, with the last step being a length 1 derivation with (q, δ) ∈ H and λ, ρ ∈ M. By the "separability" property (2), there is a factoring α'β' = λqρ, such that α → α' and β → β'. By the way the free extension M[Q] is defined, the element q ∈ Q is prime, so it must appear in either α' or β', but not both.

In the first case, the result is the factoring α' = λqε, ρ = εβ', with α → α' = λqε → λδε and β → β', resulting in (λδε)β' = λδ(εβ') = λδρ ... thereby showing that λδρ ∈ [α][β].

In the second case, the result is the factoring λ = α'ζ, β' = ζqρ, with α → α' and β → β' = ζqρ → ζδρ, resulting in α'(ζδρ) = (α'ζ)δρ = λδρ ... thereby, again, showing that λδρ ∈ [α][β].

Second, the property that [m] = {m}, for m ∈ M is trivial, since the only phrase structure rules in an algebraic grammar have non-terminals on the left-hand side, and m has no non-terminals in it, since it is of degree 0. Therefore, the only derivations m → β are the length-0 derivations where m = β. Thus, [m] = {m}.

Separable Grammars Reduce Equivalently To Algebraic Grammars This requires replacing the phrase structure rules H by an alternative, finite, set H_0 ⊆ Q × M[Q] of the required indicated form. This set is obtained by selecting out a finite set of H-derivations of this form and using them as a new basis, in such a way as to ensure that the derive sets [α] are all unaffected.

This is done by taking each phrase structure rule (α,β) ∈ H, by decomposing α = m_0 q_1 m_1 ⋯ q_n m_n, where the m's are in M, the q's are in Q, and n is the degree of the word α ∈ M[Q]. This includes the possibility that n = 0! It won't matter. Bear in mind that the factoring of α ∈ M[Q] in this way is unique, since the q's are prime in M[Q].

Then we apply separability property (2) to factor β = γ_0 β_1 γ_1 ⋯ β_n γ_n, where m_i → γ_i, for 0 ≤ i ≤ n, and q_i → β_i, for 0 < i ≤ n. Then we use separability property (1) to conclude that γ_i = m_i, for 0 ≤ i ≤ n, thereby yielding the factoring β = m_0 β_1 m_1 ⋯ β_n m_n.

Then, we replace (α,β) by the rules (q_i,β_i), for 0 < i ≤ n. The replacement leaves all the derive sets unaffected.

We carry out the same replacement with each (α,β) ∈ H, one-by-one, thereby ultimately leading to the a new set H_0 of phrase structure rules of the desired form.

The result is that the separable grammar is reduced to algebraic form, such that all the derive sets remain unchanged.

• Impressive if you wrote this yourself, a bit less if you got it from ChatGPT :-) –  Dominique Commented Apr 17 at 8:50
• You might want to try this with ChatGPT to see what happens (3.5 is open access, now, at OpenAI). I just posed the query - Can the concept of "grammar" be defined for arbitrary monoids? - just to probe its current state of knowledge. Based on what I saw, I don't think it's going to be able to make the connections to arrive at a result like this. But you can try it out and see. Maybe it will come up with something. But I wouldn't put any bets on it. –  NinjaDarth Commented Apr 17 at 18:39
• Actually, you got me curious and the query led to a longer dialogue. Still 3.5. I had to hand-hold it to get it to recognize the use of free extensions, but once that was pointed out, it suggested generalizing to allow the "start symbol" to be an arbitrary element of the free extension, not just a non-terminal. But: it initially wanted no non-terminals on the right. And when posed with the problem of generalizing away from the condition that the left-hand side be a single non-terminal, it went astray: it wanted both sides to have the same non-terminals. I'm running out of hands to hold. –  NinjaDarth Commented Apr 17 at 19:10

Context-free grammars do not consider any context for production rules. Context are either terminals or non-terminals.

So: Context-free grammars only have a single non-terminal on the left side of the production rules.

• 3 What does this add to the existing answers? Also, a production rule with two or more non-terminals on the left hand side isn't context free either. –  user7043 Commented Aug 18, 2014 at 18:47
• 1 I think the given answers are much too long. If one would add a TL;DR, I would delete this one. –  Martin Thoma Commented Aug 18, 2014 at 18:51
• 2 Nice! Would you say that the "context" is the extra characters that qualify when each production rule could be applied? –  rick Commented Aug 19, 2014 at 3:02
• 2 This answer concisely answers the question, though the other answers explain it further. –  Rod Maniego Commented Jun 27, 2022 at 11:30
• @rick Exactly that. “Context free” can have a rule “replace A with cBad” for example. “Context sensitive” can have a rule “replace A the same way, but only if A is preceded by abc and followed by xyz”. –  gnasher729 Commented Apr 17 at 13:48

Not the answer you're looking for? Browse other questions tagged programming-languages compiler grammar or ask your own question .

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Raine R, Fitzpatrick R, Barratt H, et al. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Southampton (UK): NIHR Journals Library; 2016 May. (Health Services and Delivery Research, No. 4.16.) doi: 10.3310/hsdr04160-105

Challenges, solutions and future directions in the evaluation of service innovations in health care and public health.

Essay 7 contextual issues and qualitative research.

Emma Howarth , Kelly Devers , Graham Moore , Alicia O’Cathain , and Mary Dixon-Woods .

Affiliations

The need to understand the contextual influences that subvert, neutralise, intensify or otherwise influence interventions is well recognised, yet the role that context plays in the design and evaluation of health and social care interventions remains understudied. Concerted effort from the research community – researchers, funders and publishers – is needed to address this problem. Mixed-methods studies are likely to be critical. Researchers should expend greater effort earlier in the development and evaluation process to optimise interventions, identify causal mechanisms and characterise contextual influences. The development of quantitative measures to explore structural features of context should be a future focus, as should better reporting of interventions and the development of guidance to support explicit descriptions of context. Encouraging the conduct of mixed-method process evaluations alongside trials and other evaluations will require new norms and expectations of research and associated funding commitments, attention to collaborative relationships and changes in publishing practices.

• Scientific summary

The need to understand the contextual influences that subvert, neutralise, intensify or otherwise influence interventions is increasingly well recognised, yet the role that context plays in the design and evaluation of health and social care interventions has remained understudied. Concerted effort is needed to address the conceptual, methodological and practical challenges associated with conducting contextually sensitive research.

Several key issues present barriers to progress. First, poor-quality reporting of interventions (and programmes more broadly) hampers understanding of what an intervention comprises, the mechanisms through which it works and the likely influences on implementation, including those of context. Second, the need to understand and give accounts of context is continually challenged by the problem of conceptualising what ‘context’ means in practice. Third, inadequate time is given over in the early stages of research to theorising about context.

Effort expended early in the development and evaluation process is needed to optimise the theoretical basis of interventions and to characterise the systems into which they will be introduced. Quantitative measures to explore structural features of context are essential; more effort in helping to specify these measures and support data collection will be an important focus for the future. Qualitative methods are particularly suited to securing understanding of the theoretical basis of interventions, exploring how, where, when and by whom an is intervention delivered and received, identifying effects and how they are achieved, and characterising how interventions are adapted in response to contextual influences and how contexts themselves are modified by interventions. For the future, researchers, funders and publishers should be encouraged to think in terms of mixed-methods studies where qualitative and quantitative methods are seen as complementary methods. Better reporting of interventions and the development of guidance to support explicit descriptions of context are also needed.

Realisation of these ideas will require funder commitments to adequately resource both developmental work and process evaluations that run alongside trials and evaluation designs, more realistic expectations about the time frame for developing an intervention, and journals that are willing to publish the results of this kind of work. The institutional features of the research landscape – including funding structures, publishing models, and the norms, values and practices of the researcher community and the users of evidence – also need critical scrutiny to ensure that they support contextually sensitive research.

• Introduction

Medical Research Council (MRC) guidance on developing and evaluating complex interventions recommends that researchers should ‘first develop the intervention to the point where it can reasonably be expected to have a worthwhile effect’. 1 But health and social care systems continue to be challenged by the problem that initially promising interventions often prove difficult to replicate and scale: what works in one setting may not be as effective, and may sometimes even be harmful, when implemented elsewhere. 1 Efforts to understand the contextual influences that may subvert, neutralise, intensify or otherwise influence interventions are, therefore, critical. Yet current practices of research and evaluation are pervaded by a tendency to defer thinking about ‘contextual contingencies’ until too late. 2

Participants at the London meeting in 2015, which is described in the Introduction to this volume, suggested that the sparse attention given to context in the design and evaluation of health and social care interventions 3 is explained not by the perception that context is unimportant, but by the formidable conceptual and practical challenges associated with defining, measuring and articulating the role of contextual factors and influences. Even a consensual definition of context remains elusive. The need for serious attention to questions of context is now, however, increasingly well recognised across a range of disciplines, 4 , 5 to the extent that participants emphasised the need for guidance on how to structure thinking about context and to select appropriate methods for its study.

In this essay, we propose that moving the field forward will require more sophisticated characterisation of interventions and their contexts of implementation. We emphasise the benefits of quantitative measures, but highlight the value of qualitative methods and of combining qualitative and quantitative methods when addressing questions of context.

• Understanding and reporting interventions

One major challenge in studying context and contextual influences is the persistent problem of poor-quality reporting of interventions and programmes. Good accounts of what a programme/intervention is, how it works and how it is influenced by and exerts influence on the surrounding context are rare. 6 Although the Consolidated Standards of Reporting Trials (CONSORT) requires that ‘the interventions for each group with sufficient details to allow replication, including how and when they were actually administered’ (item 5) 7 be reported’, only a minority of non-pharmacological interventions are adequately described. 8 , 9 Without a complete and explicit description of an intervention, 10 clinicians, patients, researchers and other decision-makers are left unclear about what an intervention comprises and the likely influences on implementation, including those of context. 11 This essay therefore expands on considerations regarding trial design and evaluation discussed in Essay 2 in this volume.

Publication guidelines now increasingly support the view that a complete description means that that the theoretical basis of interventions must be reported alongside the components of interventions, 11 not least because this allows an assessment of how far the intervention is theoretically well founded. It may also help in explaining the findings of studies and in optimising the design and implementation of interventions in the light of those findings. For instance, the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), a large-scale audit and feedback exercise that provides feedback to hospitals on their risk-adjusted outcomes (e.g. mortality, specific complications and length of stay), was found in a quasi-experimental study to have no additional effect on the outcomes of participating hospitals relative to matched controls after taking into account pre-existing secular trends. 12 A possible explanation for this disappointing result is that the audit and feedback mechanism used in NSQIP is not fully consistent with the findings of the significant body of research and theory on features of feedback effectiveness, including those relating to frequency of feedback, selection of solutions and setting of goals and action plans. 13 – 15

Use of theory can also help in explaining why interventions may fare differently in different contexts, as shown by studies of the quality improvement collaborative (QIC) model. The model requires organisations to come together over a defined period of time to engage in collaborative learning and exchange of insights to achieve improvement goals. 16 , 17 Although QIC models have proliferated widely, 18 understanding of whether or not they work – and, if so, how, when and where they work best – has remained underdeveloped. 17 Here, again, theories are important. An evaluation of a QIC model to improve stroke care in the north-west of England 16 using a randomised design to assess performance on nine aspects of stroke care found that hospitals participating in the QIC model showed only modest improvements on selected aspects of care compared with non-participants. A qualitative study to explore the views and experiences of those involved in designing and implementing the QIC model found that it conferred some benefits but also introduced significant tensions – many of them possible to predict using theory on collective action. 19 For example, participation required substantial effort on the part of participating organisations, introducing the risk of ‘free-riding’ where some organisations gave less than they received. Importantly, this qualitative study also identified the influences on organisations’ commitment to the collaborative ethos by characterising features of both the inner context of individual hospitals (e.g. baseline performance) and an outer context of competition over the provision of stroke services.

When descriptions of interventions and accounts of how they work are inadequate, attempts to replicate successful programmes risk disappointment that may be blamed on context, when the true culprit is that only the superficial outer appearance of the intervention has been reproduced and not the mechanisms (or set of mechanisms) that produced the outcomes in the first instance: a phenomenon known as ‘cargo cult science’. 10 This was exemplified when the success of a complex intervention that reduced central venous catheter bloodstream infections across a large number of intensive care units (ICUs) in the state of Michigan 20 was simplistically credited to the implementation of a basic checklist. 21 A post hoc analysis to discern the mechanisms through which the programme worked revealed a complex web of interacting social processes, including the technical and cultural shifts that ultimately increased patient safety. 10 A qualitative study of the attempt to replicate the programme in England suggested that the mechanisms that underpinned the programme’s success in Michigan were generally not activated in the English version, even though its components were similar. 22

These findings, like those of other studies, add weight to the recent suggestion that the fidelity of an intervention may reside in the mechanisms of action rather than in specific programme components, such that individual components of an intervention may be varied across contexts without comprising the effects of the programme. 23 , 24 This requires an understanding of the role and meaning of each intervention component, in terms of how it will interrupt the causal mechanisms which perpetuate and sustain a problem in the context being investigated, rather than simply a description of its form. 24 It also requires a deep understanding of the system into which the intervention will be placed to consider ‘fit’ between intervention and context, how the intervention will interact with context to bring about, aid or sustain the hoped for change, and the likely consequences of attempting to change the context through introducing a new intervention. The realist evaluation approach 25 is one, among several, that strongly emphasise the interaction between context and intervention, contending that programmes have differential effects because the mechanisms by which an intervention influences outcomes may not be activated in all contexts, or may differ from one context to the other.

• Understanding the role of contextual influences

The term ‘context’ has its etymological roots in the Latin contextus , meaning ‘joining together’. Understanding what happens when a particular intervention is joined together with an individual, team, organisation or health system remains a critical challenge both for science and for practice and policy. But the need to understand and give accounts of context is continually challenged by the problem of conceptualising what ‘context’ means in practice. 3 , 6 A taxonomy that could be used across multiple studies to identify and describe critical components of interventions and their contexts might be helpful, 3 as might a synthesis of evidence from a range of disciplines to identify those aspects of context that are most consistently related to intervention success and sustainability. 26 Some inroads have been made to this end in implementation science by Damschroder et al. , 27 who synthesised constructs from existing implementation theories to create a framework comprising five key domains (intervention characteristics, outer setting, inner setting, characteristics of the individuals involved and process of implementation) that can be used to guide choices about relevant aspects of the intervention, context and process of implementation to assess in any given evaluation.

To date, many intervention studies lack even basic information about the research and clinical contexts in which they are undertaken, 8 even though item 4b of the CONSORT statement (setting and location) states that researchers should report on ‘other aspects of the setting (including the social, economic, and cultural environment and the climate) [that] may also affect a study’s external validity’. 7 A specific extension to reporting guidelines to include more detail on context has been proposed. 3 , 6 Data produced by such reporting may enable the identification of candidate factors suitable for quantitative measurement and the modelling of contextual variables 28 that may be important in revealing variations and inequalities.

This kind of work may also be especially useful in exploring how structural features of contexts may moderate the mechanism through which an intervention produces its effects. For example, a cluster randomised trial of breakfast clubs delivered on a universal basis 29 found that the provision of breakfast at school had no overall impact on rates of breakfast skipping. However, further analysis found that schools in more deprived areas showed an increase in the number of healthy foods eaten at breakfast and found that rates of breakfast skipping were reduced at these schools, suggesting that context moderated the impact of the intervention. Similarly, a peer-led smoking prevention programme aiming to prevent smoking uptake among young people had a greater effect in areas characterised by dense social networks that supported the more efficient diffusion of smoking prevention messages. 30

The relationship between context and intervention is not, of course, unidirectional. Increasingly, context is seen as having effects so powerful that it may ‘shape or co-construct complex interventions and therefore cannot be considered separately from those interventions’. 3 Accordingly, Hawe et al. 23 conceive of interventions as events in systems that either ‘leave a lasting footprint or wash out depending on how well the dynamic properties of the system are harnessed’. To this end, qualitative research during the earlier stages of intervention development can be used to give insight into the context into which an intervention will be delivered, enabling researchers to better articulate what aspects of the existing context the intervention attempts to change, and why. Where this process of intentional adaption takes place, work by Jansen et al. 31 suggests that researchers may attempt to adapt the context to fit the intervention, rather than fashion the intervention to suit the setting.

Interventions may also ‘evolve’ over time, shaped by a range of contextual influences and pragmatic considerations. 3 , 32 Interventions involve attempts to change the functioning of ‘complex adaptive systems’, 23 and the survival of these systems is dependent on ceaseless adaptation in response to changing internal and external environments. Hence adaptations to interventions are often necessary because aspects of the system or its wider context may change after the study has begun, including changes in the economic or political context or local changes. 3 , 33 Mutations in an intervention may be highly functional, resulting in an intervention that is better suited to the setting in which it is delivered, although adaptations may also have unintended consequences. 3

Fundamentally, the dynamic interplay between intervention and context inherent to many complex interventions means that it is often difficult, and indeed not always helpful, to separate intervention from context. 23 Viewing interventions as attempts to change aspects of their contexts means that they cannot be understood in isolation from those contexts. Yet adaptations have traditionally been viewed simply as methodological shortcomings, particularly in relation to trials. 3 As a result, researchers may describe (often poorly) the intervention that they intended to deliver rather than that which was actually implemented, thus hampering efforts to replicate or build on research findings. Wells et al. 3 urge a cultural shift in which the open and honest reporting of issues such as local adaptations and differences in implementation is perceived as an indicator of study strength and research integrity, rather than a study weakness or lack of researcher ability: what is important is not that these adaptations occurred, but that they are ‘known, understood, and reported’. The key to this shift in thinking, Hawe et al. 34 argue, is the idea that the integrity of complex interventions is defined functionally, rather than compositionally.

As Figure 7.1 depicts, capturing the complexity and emergent nature of the interaction between context and intervention 35 is likely to require well-conducted process evaluation at all stages of intervention design, implementation and evaluation.

Key functions of process evaluation and relationships among them (green boxes represent components of process evaluations, informed by intervention description, which inform interpretation of outcomes). Reprinted from Process Evaluation of Complex Interventions: (more...)

• The role of qualitative and mixed-methods research

The MRC guidance 1 emphasises the role of qualitative methods during the development and modelling stage that precedes evaluation, thus helping to convince funders of the value of qualitative research. 36 Discussants at the 2015 London meeting felt that the exemplary case studies and synthesis of existing work, like that undertaken by O’Cathain et al. 36 and Lewin et al. , 37 play an essential role in demonstrating the value that qualitative methods bring. This was seen as especially important for participants, who reported a sense that funders historically have been reluctant to fund development and feasibility work, perhaps especially when policy-makers are eager to get on with implementing a new intervention or policy.

Further mitigating the fruitful use of qualitative research is the emphasis on high-impact publications in assessing the value of institutions, research disciplines and individual researchers. O’Cathain et al. 36 found that researchers perceived randomised controlled trials (RCTs) as more likely to facilitate this type of output, with qualitative papers more likely to be published in lower-impact journals. This often resulted in quantitative and qualitative papers being published separately and, in some cases, where qualitative papers were seen as lower-status outputs, not at all. 36 O’Cathain et al. 36 suggest a need to address publishing norms that serve as a barrier to the publication of applied mixed-methods research, and for publishers to recognise that in an output-driven environment a lack of high-quality forums for this type of research may have a detrimental influence on the work that is undertaken in the first place. O’Cathain et al. 36 also recommend that care is taken to explicitly articulate the role played by the qualitative research and the value brought to the trial (or evaluative study) as well as lessons learned for studies in the future.

An example of this kind of useful work is the study conducted by Redfern et al. , 38 who used a range of qualitative methods to develop Stop Stroke, an intervention to improve clinician and patient management of risk factors to prevent stroke recurrence. In establishing the theoretical rationale for the intervention, the research team conducted a systematic review and exploratory interviews to understand patients’ experiences of secondary prevention as well as non-participant observation of staff during clinical consultations to understand how prevention advice was given in local contexts; they also mapped patterns of risk factor management practices as well as identifying barriers and facilitators to risk factor management. The resulting programme used these data to design a complex multiple component intervention targeting patients, carers and health-care professionals with tailored advice at three time points post stroke.

This work shows how qualitative methods can be used during the development and modelling phase to design an intervention that takes into consideration the preferences of patients and practitioners, but also takes account of existing local provision. However, this kind of work-up of interventions and contexts remains relatively rare: a number of studies have highlighted the need for more effort to be expended at earlier stages of intervention development and evaluation to maximise the value that qualitative methods in particular can bring. 31 , 36 These modelling considerations are also discussed in the context of making best use of available evidence in Essay 1 .

Table 7.1 reports the diverse ways in which qualitative research is used alongside trials to explore various aspects of intervention and study design. The largest subcategory focused on exploring the feasibility and acceptability of the intervention in practice (23%), but relatively little of this work (28%) was undertaken at the pre-trial stage. Only 13% of all papers published between 2008 and 2010 reported pre-trial intervention development work and only one published paper explored the mechanism of action prior to the full trial, suggesting that this important stage of work could be given more emphasis.

Framework of the focus of qualitative research used with trials

Beyond the development phase, qualitative research that runs alongside programme implementation offers a useful way of generating an account of the programme as it happened, rather than as it was planned. 10 , 28 O’Cathain et al. 36 advocate for qualitative research to take a more challenging role, rather than one that is simply descriptive. The programme theory should be specified from the earliest point in the design and development phase 1 but with the expectation that the mechanism that is specified in advance may not necessarily hold true or may need to be adjusted. 10 , 40 Thus, for example, a study undertaken by Dixon-Woods et al. 22 to characterise the Matching Michigan programme in practice involved approximately 855 hours of observational fieldwork in 17 ICUs, including observation of training events, team meetings and external reference group meetings, over 100 interviews with ICU staff and documentary analysis. This enabled them to establish that the programme as implemented was not the same as the one on which it was modelled, and that outcomes varied as a function of outer and inner contextual influences as well as differences in data collection that undermined the comparability of supposedly standardised measures of infection rates across units. 41

Davidoff et al. 40 highlight that aspects of the broader contextual landscape such as cultural, emotional and political challenges, as well as aspects of local context, are all likely to become much more salient as the work proceeds. With this in mind, theoretical accounts of how a programme works should be developed through a continual cycle of theory testing and refinement. 1 To facilitate the enquiring and often iterative nature of qualitative research, O’Cathain et al. 36 recommend that ethics boards be more sympathetic to the evolving role of qualitative research throughout a study, allowing researchers to pursue emergent findings without requiring substantial amendments to ethical approvals.

A mapping study carried out by O’Cathain et al. 36 that explored the use of qualitative methods alongside RCTs gives some sense of how researchers may employ mixed-methods designs to study complex interventions. The potential value of the qualitative research to the endeavour of generating evidence of effectiveness of health interventions was found to be considerable, and included improving the external validity of trials, facilitating interpretation of the trial findings, improving sensitivity to the human beings who participate in trials, and saving money by steering researchers towards interventions more likely to be effective in future trials. 39 Some researchers perceived qualitative research as peripheral or an ‘add-on’ to the trial, but others saw the qualitative research as essential and of equal value to the trial. Those researchers who were interested in influencing practice in the real world, and who were aware of the complexity of the intervention they were testing, were particularly likely to see qualitative research as essential; accordingly, they were more likely to attribute more resources to the qualitative research and work within integrated teams. Importantly, they were also more likely to value the integration of the qualitative and quantitative findings, thus enhancing the impact of the qualitative findings on the current or future trial.

These challenges need to be addressed given mounting evidence of the benefits of qualitative research in ensuring success in the design and implementation of interventions and in ensuring adequate accounting for context. Case study methodology, particularly when comparative, may be especially powerful in producing in-depth, multifaceted understandings of a complex issue in its real-life context. 28 For example, a prospective longitudinal ethnographic study undertaken by Hoddinott et al. 42 alongside a cluster randomised trial of a group intervention to facilitate breastfeeding in Scotland combined qualitative data with descriptive quantitative data on intervention ‘dose’ to construct case studies, enabling comparison of policy implementation and impact across seven different localities. The techniques used in Hoddinott et al. ’s study 42 allowed the development of a model of health service attributes necessary to successfully deliver the policy. For instance, in localities where breastfeeding rates had declined, negative aspects of place (rising deprivation, availability of other group interventions, lack of suitable venues), personnel resources and organisational change predominated, preventing teams from creating or exploiting opportunities for the multidisciplinary partnership working that was crucial for successful implementation. The study design also enabled a full analysis of implementation processes prior to knowing the trial outcomes, thus minimising retrospective selection bias and the kinds of rationalisations that can occur when questions about failure (of the technical intervention or the implementation strategy) are asked in retrospect. 42 As this study shows, the attribution of causality in case studies can be supported by iterative pattern-matching processes that develop explanations, deduce implications of those explanations and seek additional information to check these explanations out. See further considerations regarding implementation in Essay 8 of this volume.

Their evaluation of the Multi-Payer Medical Home Demonstration, RTI International, The Urban Institute and the National Academy of State Health Policy 33 also employ multimethod case studies with the express purpose of building a detailed picture of the state context and programme structure that can be used to interpret variation in implementation and outcomes. Advanced primary care practices, or ‘medical homes’, utilise a team approach to care, and aim to improve the quality and co-ordination of health-care services through emphasis on prevention, health information technology, care co-ordination and shared decision-making among patients and their providers. The demonstration will evaluate whether or not advanced primary care practice will reduce unjustified utilisation and expenditures, improve the safety, effectiveness, timeliness and efficiency of health care, increase patient decision-making and increase the availability and delivery of care in underserved areas in participating practices, compared against matched controls. Emerging findings already suggest that context has a bearing on the extent to which practices are able to implement improvement practices. For example, smaller practices and practices in rural areas with provider shortages (e.g. specialists, dentists, mental health providers) and a lack of patient transportation both encountered barriers that practices could not always overcome. Preliminary outcome data suggest some variation across states in health-care utilisation and expenditure, and subsequent investigation will examine whether or not differences at the state, practice and programme levels can account for this. This work highlights how qualitative evidence produced as part of a mixed-methods evaluative study has the potential to be of huge interest to policy-makers, especially where it can be used as a formative mechanism of feedback to augment aspects of the context or intervention that are likely to improve implementation in ‘real time’. 1 , 30 , 43

This raises an important point, however, about the role of evaluators in feeding back information during the course of a study. Formative feedback loops may be most easily accommodated during the feasibility and pilot stages of evaluation, but researchers may assume a more passive role in studies that aim to establish effectiveness under real-world conditions to avoid interfering with implementation and changing how the intervention is delivered. Ambiguity about the information that can be expected to be fed back, when, to whom and in what level of detail can create tensions between the different stakeholder groups involved in a collaborative evaluative endeavour. 43 , 44 It is, therefore, important to establish systems for communicating process information to key stakeholders at the outset of the study, to avoid perceptions of undue interference or that vital information was withheld. 43 To this end Brewster et al. 44 have developed a concordat or framework that is designed to be completed collaboratively between evaluation partners with the aim of setting expectations and resolving conflict as it arises during evaluation activity. The process of completing a joint mandate for working appears to be a constructive way of building local ownership and shared vision and emphasising the interdependency of all parties. 44

Despite the evident value that qualitative evidence brings to the development and evaluation of complex interventions, some participants at the 2015 meeting in London felt that a bias towards favouring quantitative research was pervasive. A number of structural issues were identified by O’Cathain et al. 36 as driving the privileging of quantitative over qualitative methods, many of which resonated with the views of researchers participating in the meeting in London. They spoke of a historical climate of underfunding qualitative research, which O’Cathain et al. 36 identified as a function in part of researchers either underestimating the cost of the research, or deliberately ‘squeezing’ the qualitative budget in an attempt to keep the costs of a bid down. At the same time, participants also described the tension created by the unrealistic expectations placed on qualitative research to answer all questions not addressed by quantitative methods, 45 especially where qualitative research is undertaken retrospectively to explain intervention failure.

• Conclusions

Context has remained understudied in comparison with interventions. An urgent task is that of deepening the conceptual underpinnings of the interactions between contexts and interventions and developing methods that allow for their exploration and evaluation. In understanding interventions as attempts to change their contexts, theorising about context could, for instance, become the starting point for approaches to intervention and evaluation design that are rooted in understanding of how the problem of interest is created and maintained in a particular community organisation or system. 34 This kind of thinking may help to challenge the currently dominant assumption that the ‘best’ or the ‘ideal’ comes from the laboratory and gets progressively compromised in real-world applications, and instead force attention onto the need to avoid simplistic and etiolated accounts of what drives change. 10

It is now clear that a description of the components of an intervention is not enough to ensure replication and scaling: what matters is likely to be the activation of mechanisms, even if precise activities undertaken to activate those mechanisms differs across contexts. Thinking in this way requires far more attention to the upfront specification and piloting of programme theories and to the characterisation of contexts. It also requires the subsequent updating of these in the light of process evaluations that are explicitly attentive to context.

Poorly reported interventions compound the problems of weak accounting for context. Recent efforts to promote improved reporting includes a checklist to prompt authors to describe interventions (including comparator interventions) in sufficient detail to allow their replication. 11 Comprising 12 items, the Template for Intervention Description to Enable Replication (TIDieR) encourages authors to describe features including which interventions (or control conditions) are delivered to different groups (e.g. materials, procedures, provider, mode of delivery, setting, dose), as well to explain legitimate variants of the intervention as well as unplanned adaptations that occur during the course of delivery. However, further work to support explicit descriptions of context is needed.

Such work should identify the benefits of mixed-method approaches to the study of context. Quantitative measures to explore structural features of context are essential, and more effort in helping to specify these measures and support data collection will be an important focus for the future. Qualitative methods are particularly suited to securing the theoretical basis of interventions, exploring the process by which an intervention or programme is delivered, identifying effects and how they are achieved, and characterising how interventions are adapted in response to contextual influences and contexts themselves are modified by interventions. However, when using mixed methods, researchers need to be sensitive to the fact that qualitative and quantitative research methods have grown out of different paradigms. The strengths of each method should not simply be used to bolster the weakness of the other, but instead should be used as complementary methods to study different phenomena related to the same question. 46 , 47

Deepening understanding of the contextual factors that influence the delivery and function of complex interventions will require new norms for the conduct of evaluations of complex interventions. Gathering the rich and varied multimethod data needed to conduct a high-quality evaluation involves close collaboration with intervention developers and implementers. The different goals, perspectives, expectations, priorities and interests, professional languages and norms of practice of these groups may lead to tensions that need to be acknowledged and managed from the outset.

For the future, researchers and funders should be encouraged to think in terms of mixed-methods studies where qualitative and quantitative methods are seen as complementary methods, and to expend greater effort earlier in the development and evaluation process to optimise interventions, identify causal mechanisms and characterise contextual influences. Realisation of these ideas will require funder commitments to adequately resource development work and process evaluations that run alongside trials and other types of outcomes-focused evaluations, more realistic expectations about the time frame for developing an intervention, and journals that are willing to publish the results of exploratory work.

Despite current challenges, there was a sense of an improving climate in which qualitative work was valued. O’Cathain et al. 36 highlighted the explicit value placed on qualitative methods and mixed-methods studies in the MRC evaluation framework, 1 and the inclusion of qualitative and mixed-methods researchers on research commissioning panels as key drivers of this positive shift. Participants in the London meeting proposed that current calls for examples of impact (by research councils, Research Excellence Framework, the National Institute for Health Research) should be used to further highlight the contribution of qualitative research and the value of mixed-methods studies.

Finally, it is clear that institutional features of the research landscape – including funding structures, publishing models, and the norms, values and practices of the researcher community and the users of evidence – need critical scrutiny in order that they support contextually sensitive research that is capable of determining if and how complex interventions have their desired effects on health and social care services and systems, and, ultimately, on the people who use them.

• Acknowledgements

Contributions of authors

Emma Howarth (Senior Research Associate, Public Health) wrote the first draft of the essay and contributed to the production of subsequent drafts.

Kelly Devers (Senior Fellow, Health Policy) commented on the draft essay.

Graham Moore (Senior Lecturer, Social Sciences and Health) edited the essay and provided additional material.

Alicia O’Cathain (Chair in Health Services Research, Applied Health Research) edited the essay and provided additional material.

Mary Dixon-Woods (Professor of Medical Sociology) contributed to the writing of the first draft of the essay and produced the final draft.

• List of abbreviations

Consolidated Standards of Reporting Trials

intensive care unit

Medical Research Council

National Surgical Quality Improvement Program

quality improvement collaborative

randomised controlled trial

Declared competing interests of authors: Kelly Devers served as the Urban Institute Project Director for the Children’s Health Insurance Program Reauthorization Act Quality Demonstration Evaluation briefly described in the manuscript. She also served as the qualitative, cross-state and Programme Assistance team lead on the Centre for Medicare and Medicaid Innovation Multipayer Advanced Primary Care Practice Demonstration Evaluation briefly described in the manuscript. However, her observations in this essay are solely her own and do not reflect the opinions or perspectives of any federal agency or the Urban Institute.

This essay should be referenced as follows: Howarth E, Devers K, Moore G, O’Cathain A, Dixon-Woods M. Contextual issues and qualitative research. In Raine R, Fitzpatrick R, Barratt H, Bevan G, Black N, Boaden R, et al . Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Health Serv Deliv Res 2016; 4 (16). pp. 105–20.

Included under terms of UK Non-commercial Government License .

• Cite this Page Howarth E, Devers K, Moore G, et al. Contextual issues and qualitative research. In: Raine R, Fitzpatrick R, Barratt H, et al. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Southampton (UK): NIHR Journals Library; 2016 May. (Health Services and Delivery Research, No. 4.16.) Essay 7. doi: 10.3310/hsdr04160-105
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Overview of Research Gaps for Selected Conditions in Women's Health Research at the National Institutes of Health

Proceedings of a workshop—in brief.

A National Academies committee held a public hybrid workshop in March 2024 on the state of women's health research for select conditions and to hear perspectives from the public. Topics included the science of sex differences and research needs in women's cancers, reproductive and gynecologic health, and mental and behavioral health.

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National Academies of Sciences, Engineering, and Medicine. 2024. Overview of Research Gaps for Selected Conditions in Women's Health Research at the National Institutes of Health: Proceedings of a Workshop—in Brief . Washington, DC: The National Academies Press. https://doi.org/10.17226/27932. Import this citation to: Bibtex EndNote Reference Manager

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Is Iced Tea Good for You?

This summer favorite may be refreshing, but it's not always so healthy. Here are better choices.

Hot days and cool, crisp iced tea seem like a match made in heaven. And plenty of people agree.

According to the Tea Association of the USA, Americans consumed nearly 4 billion gallons of tea in 2023, and 75 to 80 percent of it was iced tea.

Freshly made iced tea can have many of the same health benefits as hot tea , namely the potential to improve heart health and lower the risk of conditions such as cognitive decline and diabetes. The same can’t be said for many bottled iced teas.

"In order to have these beneficial effects, the tea you’re drinking must be high in antioxidants," says Joe Vinson, PhD, professor of chemistry at the University of Scranton in Pennsylvania. "And there’s no way of knowing what you’re actually getting when you buy iced tea in a bottle."

What’s more, many bottled iced teas are nutritionally on a par with soda—complete with loads of added sugars, and artificial colors and flavors.

That’s not to say a ready-to-drink iced tea is never a good choice. Some bottles are better than others, and, as always, it pays to read the nutrition label to find the best options.

Here’s what to consider before choosing a bottle of iced tea.

• Added Sugars
• Antioxidants
• Green Iced Tea
• Decoding Labels
• Brewing Your Own

Beware of Added Sugars

"You may think you’re choosing a healthier option when you grab a bottle of iced tea instead of a soda, but in many cases, you’re getting about as much or more sugars," says Amy Keating, RD, a nutritionist at Consumer Reports. 

The current Dietary Guidelines for Americans recommends that less than 10 percent of your daily calories come from added sugars—that’s less than 50 grams if you’re following a 2,000-calorie diet. The American Heart Association says that the maximum daily added sugars intake should be no more than 25 grams for women and 36 grams for men.

Drinking sweetened iced tea can push you close to or over those limits. For example, an 18.5-ounce bottle of Pure Leaf Lemon Tea has 38 grams of added sugars and 150 calories, while a 20-ounce bottle of Arizona Peach Tea has 60 grams of added sugars and 250 calories.

The same goes for some iced tea/lemonade brands. Snapple Half ’n Half, for instance, has 51 grams of added sugars and 210 calories in 16 ounces. Compare those numbers with the 240 calories and 65 grams of added sugars in a 20-ounce bottle of Coke. 

Some brands will tout that they contain real sugar, cane sugar, or honey. But such products aren’t necessarily better for you than those with sugar or high fructose corn syrup. "It’s still all added sugars," Keating says.

Diet iced teas are low in sugar and calories, but they may contain sugar substitutes , such as aspartame or sucralose. Recent research suggests that consumption of alternative sweeteners may be linked to increased risk of conditions such as heart disease, diabetes, and obesity.

Your best bet is to look for iced teas labeled "unsweetened." These may have flavors (such as lemon) added, but they’ll be calorie-free and won’t contain any type of sweetener.

"If you like, mix in a teaspoon of sugar or agave syrup," Keating says. "You’ll get some sweetness, but with a lot less sugar than in a presweetened version." 

Don't Count on Getting Antioxidants

If part of the reason you drink iced tea is to get a daily dose of antioxidants, be warned that there might not be many in a bottle.

"Bottled teas are very low in antioxidants compared to freshly brewed tea," says Vinson, whose lab has analyzed a variety of teas.

In his testing, black tea bags steeped for 5 minutes in hot water contained the highest amount of antioxidant polyphenols—600 mg per cup—compared with bottled black tea, which contained just 68 mg per cup.

Vinson theorizes that the ratio of water to tea is higher in bottled teas than in tea you make at home, so the resulting beverage has a lower antioxidant level. Flavorings and sugars eliminate tea’s naturally bitter taste but may also dilute antioxidants. 

Don't Think Green Is Better

There are many things to like about green tea . Some research suggests that the type of antioxidant in green tea called epigallocatechin gallate (EGCG) may help lower the risk of certain cancers and reduce the risk of heart disease.

But don’t be blinded by green tea’s health halo. Many bottled green teas—just like bottled black teas—are loaded with added sugars. For example, an 18.5-ounce bottle of Gold Peak Green Tea has 38 grams of added sugars and 150 calories.

Know How to Decode Labels

Claims on iced tea bottles can make you think that the product is healthier or better in other ways, but it pays not to take them at face value. Here’s what a few of the most common terms you see really mean.

Tad sweet or slightly sweet: These unregulated terms are open to the manufacturer’s interpretation. Usually, it means that the tea has less sugar than a similar product from the same brand, but the amounts vary from brand to brand. The teas with this claim that we looked at contained anywhere from 5 to 25 grams of added sugars per bottle.

No artificial sweeteners: The tea may still contain sugar, stevia, sugar alcohols like erythritol, or a combo. For example, Pure Leaf Honey Green Iced Tea carries this claim and has 25 grams of sugar per 18.5-ounce bottle (from sugar and honey), as well as stevia extract. This sweetener is thought of as “natural” (vs. artificial), but while it comes from a plant, getting the extract requires processing in a lab.

No sugar, sugar free, or zero sugar: By law, the product must have less than 0.5 gram of sugars per serving. Teas with these claims may still contain a sugar substitute, such as sucralose, so check the ingredients label.

Fair Trade Certified: Farmers who grow crops (like tea or sugar) used in the product must pay workers at least the local minimum wage, provide safe working conditions, and more. This claim is verified, but it may apply to just a single ingredient, say, the tea and not the sugar.

Rainforest Alliance Certified: This means that some or all of the tea is sourced from farms that have met standards promoting sustainability and protecting farmers, forests, wildlife, and local communities.

Consider Brewing Your Own Iced Tea

It will cost less, and you can control the amount of added sugars it contains. Or skip the sugar entirely and add a splash of fruit juice or a purée of fruit (like peaches or raspberries). Herbs and spices such as mint, basil, cinnamon, and ginger can also add a depth of flavor to the tea.

Making iced tea is simple. You can pour boiling water over a tea bag or loose-leaf tea (black or green), allow it to steep for about 5 minutes, and let it cool in the refrigerator. (To make a pitcher, steep eight to 10 tea bags in 2 quarts of water, then refrigerate.) You can also add tea to cold water and let it steep for about 2 hours. A 2016 study published in the Journal of Food Science found little difference in the polyphenol content of black or green tea steeped in hot water for 5 minutes vs. cold water for 2 hours. However, using hot water may result in a stronger brew.

It’s worth noting, though, that the antioxidants in tea will dissipate over time. "We found that once brewed, the antioxidant content went down about 10 percent a day," Vinson says. So don’t brew up a bigger batch than you can drink in a day or two.

Sally Wadyka

Sally Wadyka is a freelance writer who contributes to Consumer Reports, Real Simple, Yoga Journal, and the Food Network on topics such as health, nutrition, and wellness.

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Strengthening akis for sustainable agricultural features: insights and innovations from the european union: a literature review.

1. Introduction

2. materials and methods, 2.1. data collection procedure, 2.2. identification criteria, 2.3. screening and selection criteria, 2.4. eligibility and inclusion criteria.

• The studies that were carried out or considered the 28 countries in the European Union (including the United Kingdom until 2019 and excluding Romania).
• Studies published in the English Language.
• Studies that were published within the past 11 years (the review covers the period from 2014 to 2024, a period in which the two previous Programming Periods of the Common Agricultural Policy were implemented).
• Studies covering the inclusion of a transparent description of the process of data acquisition and interpretation.
• Studies covering a primary or secondary class investigation on the subject matter.
• Studies showcasing the effects of AKISs and FASs on agricultural knowledge advancement.
• Studies published in a non-English language.
• Studies carried out outside the EU.
• Studies with unclear methodology of data collection and analysis.
• Studies lacking author names and affiliation.
• Studies not covering both the main issues of this review (i.e., AKIS and FAS).

4. Discussion

4.1. akis and fas in the foreground through the new cap, 4.2. improving the effectiveness of an akis, 5. conclusions, author contributions, institutional review board statement, data availability statement, conflicts of interest.

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Share and Cite

Kountios, G.; Kanakaris, S.; Moulogianni, C.; Bournaris, T. Strengthening AKIS for Sustainable Agricultural Features: Insights and Innovations from the European Union: A Literature Review. Sustainability 2024 , 16 , 7068. https://doi.org/10.3390/su16167068

Kountios G, Kanakaris S, Moulogianni C, Bournaris T. Strengthening AKIS for Sustainable Agricultural Features: Insights and Innovations from the European Union: A Literature Review. Sustainability . 2024; 16(16):7068. https://doi.org/10.3390/su16167068

Kountios, Georgios, Spyridon Kanakaris, Christina Moulogianni, and Thomas Bournaris. 2024. "Strengthening AKIS for Sustainable Agricultural Features: Insights and Innovations from the European Union: A Literature Review" Sustainability 16, no. 16: 7068. https://doi.org/10.3390/su16167068

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