roy adaptation theory research papers

Roy’s adaptation model: principles and general applications

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K. Dorsey &
S. Purcell

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The adaptation model by Sister Callista Roy was developed when, as a graduate student, she was challenged to develop a theory for nursing. During that time her studies were supervised by Dorothy Johnson at the University of California. Roy began by conceptualizing the goal of nursing as facilitating the adaptation of persons to their environment. Utilizing research literature dealing with stress and coping she provided the scientific basis for her ideas.

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The Resilience-Enhancing Stress Model: A Practice Overview and Guide

The resilience of nursing staffs in nursing homes: concept development applying a hybrid model

Introducing the resilience-enhancing stress model.

Dickoff, J. and James, P. (1968) Theory is a practice discipline. Nurs. Res. , 17 , 415–35.

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Dohrenwend, B. P. (1961) The social psychological nature of stress: a framework for causal inquiry. J. Abnorm. Social Psychol. , 212 , 294–302.

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Driever, M. (1976) Problem of self-esteem, in Introduction to Nursing and Adaptation Model (ed. C. Roy), Prentice Hall, Englewood Cliffs, NJ.

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Dunn, H. (1971) High Level Wellness , R. W. Beatty, Arlington, VA.

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Lazarus, R. S. (1966) Psychological Stress and the Coping Process , McGraw Hill, New York.

Mechanic, D. (1970) Some problems in developing a social psychology of adaptation to stress, in Social and Psychological Factors in Stress (ed. J. McGrath), Holt, Rinehart and Winston, New York.

Roy, C. (1976) Introduction to Nursing: an Adaptation Model , Prentice Hall, Englewood Cliffs, NJ.

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Selye, H. (1978) The Stress of Life , McGraw Hill, New York.

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Dorsey, K., Purcell, S. (1990). Roy’s adaptation model: principles and general applications. In: Psychiatric and Mental Health Nursing. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-3011-8_9

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J Breast Health
v.10(3); 2014 Jul

Nursing Approach Based on Roy Adaptation Model in a Patient Undergoing Breast Conserving Surgery for Breast Cancer

Figen erol ursavaş.

1 Department of Nursing, Çankırı Karatekin University School of Health Service, Çankırı, Turkey

Özgül Karayurt

2 Department of Nursing, Dokuz Eylül University Faculty of Nursing, İzmir, Turkey

Özge İşeri

3 Department of Nursing, Gaziosmanpaşa University, Tokat School of Health Service, Tokat, Turkey

The use of models in nursing provides nurses to focus on the role of nursing and its applications rather than medical practice. In addition, it helps patient care to be systematic, purposeful, controlled and effective. One of the commonly used models in nursing is Roy Adaptation Model. According to Roy adaptation model, the aim of nursing is to increase compliance and life expectancy. Roy Adaptation Model evaluates the patient in physiologic mode, self-concept mode, role function mode and interdependence mode aiming to provide holistic care. This article describes the use of Roy Adaptation Model in the care of a patient who has been diagnosed with breast cancer and had breast-conserving surgery. Patient data was evaluated in the four modes of Roy adaptation model (physiologic, self-concept, role function, and interdependence modes) and the nursing process was applied.

Introduction

Breast cancer is a malignant tumor that originates from breast cells. Breast cancer is the most common type of cancer in American women, except skin cancer. According to the most recent data from the United States, 232.340 (29%) new cases of breast cancer was diagnosed in 2013, and it is estimated that approximately 39,620 of those will die due to breast cancer ( 1 ). According to 2011 data from the Ministry of Health in Turkey, breast cancer ranks top among women with an incidence of 45.1 per hundred thousand ( 2 ).

Nurses’ providing holistic care in the care of women with breast cancer is very important. Nurses can achieve in providing holistic care only with the use of nursing models. By using these models, nursing activities shift away from being service-centered to serving in a patient-focused manner ( 3 ). In addition, basic concepts and relationships between concepts are determined, problems are identified and solutions can be developed ( 4 ). In this way, nurses focus on the role of nursing and its applications rather than medical practice ( 5 ). Models not only ensure purposeful, systematic, controlled and effective patient care but also create a common language. In addition, by helping nurses to organize daily care it creates the opportunity to give high quality care with less labor ( 6 ). Despite the increase in the interest in models, use of these models in nursing practice is not common. The most important reasons for this is not emphasizing models as part of basic nursing education and most models having a complex structure including abstract concepts. For this reason, understanding and applying models by clinical nurses can be time-consuming ( 7 ). One of the widely used models in nursing is Roy Adaptation Model (RAM). RAM has been contributing to nursing practice, research, education and management and has been providing model-based information for the last 35 years ( 8 ).

In our country, the use of models in nursing practice and research has gained importance in the recent years ( 6 , 7 , 9 – 12 ). Nurses’ providing care to patients by using a model will provide holistic care. This article aims to explain application of RAM in care of a patient who has been diagnosed with breast cancer and had undergone breast-conserving surgery. Determination of nursing diagnoses and applications according to RAM forms the basis of this article.

Conceptual Framework: Roy Adaptation Model

The Roy Adaptation Model is a model widely used in the identification of conceptual foundation of nursing. The development of the model began in the late 1960s ( 3 ). Nurses from the United States and countries all around the world aim to emphasize and explain RAM as well as expanding its concepts ( 3 ). Roy defined nursing as a scientific and humanistic profession, and emphasized the importance of their specialized knowledge in contributing health needs and well-being of the community ( 3 , 13 ). According to RAM, the purpose of nursing is to increase compliance and life expectancy.

The metaparadigm concepts of Roy Adaptation Model are person, environment, health and nursing ( 4 ). The Roy Adaptation Model sees the person as a biopsychosocial being in continuous interaction with a changing environment. The environment includes focal, contextual and residual stimuli. A focal stimulus is the confrontation with one’s internal and external environment. The individual immediately resists these internal and external stimuli. Nurses aim to manage the focal stimulus first, and then the contextual stimuli ( 3 , 4 , 14 ). The contextual stimuli are those other stimuli that contribute to the focal stimuli and affect the current situation ( 3 ). The residual stimuli are closed factors affecting the current situation. These are beliefs, behaviors and personal experiences. They originate from the past and affect the response to treatment ( 3 , 14 ). Health is an anticipated dimension of human life and represents a health-disease continuum ( 3 ). Roy defined health as the process of being and becoming an integrated and complete person ( 14 ). Roy’s model of nursing defined nursing as a science, and the adaptation of this scientific knowledge into the practice of nursing ( 14 ). According to RAM, the purpose of nursing is to ensure adaptation. Increasing adaptation during health and disease improves the interaction between the environment and human systems and thus improves health. Therefore, it contributes to health, quality of life and end of life care ( 3 ). RAM also expressed specific activities distinguishing nursing from other disciplines as the nursing process. Roy proposed a problem solving approach in this process and explained this approach in five stages. These are “1. Assessment of behavior and stimuli, 2. Diagnosis, 3. Goal setting, 4. Planning, 5. Intervention and evaluation” ( 14 ).

The Roy Adaptation Model defined the innate and acquired coping processes as two sub-systems ( 3 ). The regulator subsystem consists of neuro-chemical and endocrine response. Internal and external stimuli include social, physical and psychological factors. The cognator subsystem is related more to attention, memory, learning, problem solving, decision-making, excitement, and defense status ( 3 , 13 ).

The four modes of adaptation defined in Roy Adaptation Model are physiologic, self-concept, role function and interdependence modes. Nurses help to meet the needs of individuals in these modes of adaptation ( 3 , 13 ).

The Physiologic Mode

The physiologic mode is associated with the physical answers of the person, given to stimuli from the environment ( 14 ). It includes the physical and chemical processes within an individual’s life and activities. Physiologic mode requirements are based on physiologic integrity. This mode consists of nine basic physiologic requirements: 1) Oxygenation, 2) nutrition, 3) Elimination, 4) activity and rest, 5) Protection, 6) Senses, 7) Fluid-electrolyte and acid-base balance, 8) Neurologic function and 9) Endocrine function. Physiological integrity is an essential requirement for physiologic mode ( 3 ). Table 1 represents the classification of the individual’s positive physiologic mode adaptation indicators according to RAM.

The classification of the individual’s positive physiologic mode adaptation indicators

Physiologic mode

Roy C. The roy adaptation model. 3 rd . ed. Upper Saddle River New Jersey, Pearson Education, 2009( 3 )

The Self Concept Mode

The self-concept mode is defined as the individual’s mixture of beliefs and feelings about himself or others at a certain time. The self-concept mode consists of the physical self and personal identity. Physical self contains body image and body sense. Personal identity is formed by their thoughts, moral-ethics and spirituality ( 3 ). Table 2 represents the individual’s positive self-concept mode adaptation indicators according to RAM.

The classification of the individual’s positive self-concept mode adaptation indicators

Self-concept mode

Roy C. The roy adaptation model. 3 rd. ed. Upper Saddle River New Jersey, Pearson Education, 2009( 3 )

The Role Function Mode

The role function mode covers the individual’s role in society for social integrity. The roles described herein are divided in three: 1. primary roles; the role of gender (female, male), 2. secondary roles; different roles (mother, father, teacher, etc.), and 3. tertiary roles (president of an association, etc.) ( 3 ). The basic requirement for role function mode is defined as social integrity ( 14 ). Role function mode applies to both individuals and groups at all levels. It includes designated and informal roles ( 15 ). Table 3 represents the individual’s positive role function mode adaptation indicators according to RAM.

The classification of the individual’s positive role function mode adaptation indicators

Role function mode

The Interdependence Mode

The behavior and mutual relations of individuals and groups are explained by interdependence in RAM. The interdependence mode includes relationships with others that are meaningful to the person, and support systems. For the individual, this mode focuses on interactions related to love, respect, giving and receiving value. The basic requirement of this area is the creation of a sense of confidence by relationship integrity and improvement of relationships ( 3 , 13 ). Proficiency in relationships forms the basis of group requirements. The components of interdependence mode for groups are the situation, infrastructure and the capability of members. The external context includes economic, social, political and cultural factors, while the internal context includes the mission, aim, vision, value, belief and goals of the group. For each group, the infrastructure involves both official and unofficial processes, operations and system interactions. Their capability includes cognitive coping skills, knowledge, skill, behavior and responsibilities. The processes that underlie this area are competence in relationships, development and resource ( 15 ). Table 4 represents the individual’s positive interdependence mode adaptation indicators according to RAM.

The classification of the individual’s positive interdependence mode adaptation indicators

Nursing approach based on Roy Adaptation Model in a patient undergoing breast conserving surgery

The patient had undergone breast conserving surgery (BCS) 45 days ago and was interviewed in the first course of her chemotherapy treatment. A verbal informed consent was obtained from the patient before the interview. Patient demographic and clinical data are given in Figure 1 . Patient data related to physiologic mode of RAM and nursing interventions are depicted in Figure 2 . Patient data related to self-concept mode of RAM and nursing interventions are depicted in Figure 3 . Patient data related to role function mode of RAM and nursing interventions are depicted in Figure 4 . Patient data related to interdependence mode of RAM and nursing interventions are depicted in Figure 5 .

An external file that holds a picture, illustration, etc.
Object name is jbh-10-3-134-g01.jpg

Patient demographic and clinical data

BCS: Breast conserving surgery; USG: Ultrasonography; MRI: Magnetic Resonance Imaging

An external file that holds a picture, illustration, etc.
Object name is jbh-10-3-134-g02.jpg

Patient data related to physiologic mode of RAM and nursing interventions

An external file that holds a picture, illustration, etc.
Object name is jbh-10-3-134-g03.jpg

Patient data related to self-concept mode of RAM and nursing interventions

An external file that holds a picture, illustration, etc.
Object name is jbh-10-3-134-g04.jpg

Patient data related to role function mode of RAM and nursing interventions

An external file that holds a picture, illustration, etc.
Object name is jbh-10-3-134-g05.jpg

Patient data related to interdependence mode of RAM and nursing interventions

In this article, nursing interventions including holistic care according to the Roy Adaptation Model were described, with a patient who had undergone breast conserving surgery. Being diagnosed with and undergoing treatment for cancer may lead to bio-psycho-social problems. It is expected that the development of interventions specific to the individual by nurses, will yield positive results in the adaptation of patients who are trying to cope with these problems. By this means, the patients will be adapted to their new life and their quality of life will improve. The utilization of theories will facilitate the situation and will provide a means for the nurses to focus on their profession of nursing, and develop a holistic care in biopsychosocial approach to the patients they are taking care of. For that reason, it is essential that the use of theories in nursing care, should be encouraged and their implementation into practice should be enhanced.

Conflict of Interest: No conflict of interest was declared by the authors.

Peer-review: Externally peer-reviewed.

Informed Consent: Informed consent was taken from the patients .

Author Contributions: Concept - Ö.K.; Design - F.E.U., Ö.K., Ö.İ.; Supervision - Ö.K., Ö.İ.; Funding - F.E.U.; Materials - F.E.U.; Data Collection and/or Processing - F.E.U., Ö.İ.; Analysis and/or Interpretation - F.E.U., Ö.İ., Ö.K.; Literature Review - F.E.U., Ö.K.; Writer - F.E.U. Ö.K., Ö.İ.; Critical Review - Ö.K., Ö.İ.

Financial Disclosure: The authors declared that this study has received no financial support.

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A CRITICAL ANALYSIS OF USING ROY’S ADAPTATION MODEL IN NURSING RESEARCH

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The Roy Adaptation Model: a consideration of its properties as a conceptual framework for an intervention study

Affiliation.

1 Department of Nursing and Community Health, Glasgow Caledonian University, Scotland.
PMID: 8933258
DOI: 10.1111/j.1365-2648.1996.tb02934.x

The underlying premise of this paper is the belief that one of the core values of nursing research is its ability to demonstrate that nursing interventions influence care outcomes. From this perspective the contribution offered by the Roy Adaptation Model for Nursing within an intervention study is evaluated. Although it was believed that the model enriched the study in several ways, its overall contribution was likened to a "conceptual stepping stone', used by the researcher en route back towards its underlying theories and classical intervention design methodologies.

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Published: 03 June 2024

The enablers of adaptation: A systematic review

Tia Brullo ORCID: orcid.org/0000-0003-1293-3257 1 ,
Jon Barnett ORCID: orcid.org/0000-0002-0862-0808 1 ,
Elissa Waters 2 &
Sarah Boulter 3

npj Climate Action volume 3 , Article number: 40 ( 2024 ) Cite this article

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Climate-change adaptation
Climate-change policy

Knowledge of the practice of climate change adaptation is slowly shifting from a focus on barriers and limits to an understanding of its enablers. Here we take stock of the knowledge on the enablers of adaptation through a systematic review of the literature. Our review of empirical articles explaining how adaptation is enabled finds that there is a tendency in the literature to focus on local-scale case studies. Across all studies, some factors seem to be more important than others, including resources (particularly money), awareness of climate risks and responses, leadership, bridging and bonding social capital, and the support of higher-level institutions. Our analysis also highlights significant gaps in knowledge about enablers, including those that affect change in regional/provincial and national governments, in the private sector, and in non-local not-for-profit and non-governmental organisations.

A systematic global stocktake of evidence on human adaptation to climate change

A global assessment of actors and their roles in climate change adaptation

Developing countries can adapt to climate change effectively using nature-based solutions

Introduction.

Over the past two decades, considerable effort has been devoted to identifying the barriers to climate change adaptation, with the intention of overcoming the impediments to institutional changes that reduce vulnerability 1 , 2 , 3 . Less prominent, but of growing importance, is research that explores the factors that create and promote opportunities for adaptation action 4 . The published research on these “enablers” of climate change adaptation has grown in recent years 5 , 6 . In this paper, we present the results of a systematic review of the literature on the enablers of climate change adaptation in human systems. We focus on empirical studies that identify factors that enabled the implementation of adaptation to reduce the vulnerability of people and social systems. This review seeks to understand how adaptation practitioners might positively influence the adaptation cycle, to understand the scope of current empirical literature and to identify gaps in existing knowledge on enabling adaptation.

Search Criteria

We conducted a systematic search of the Scopus database for peer reviewed literature on enablers of climate change adaptation. The purpose of this review was to analyse the existing knowledge of factors shown to enable climate change adaptation, identifying key trends and gaps that have emerged in recent years. A process of trial and error was used to identify the most appropriate search terms, which are shown in Table 1 .

The key search terms used for this review are applicable to a variety of other contexts where searching title, abstract, and key words returned over 27,000 results, hence these terms were searched in title-only to help limit results to the most relevant. This reflects the sparse and diverse literature on adaptation and the challenges of using systematic approaches in adaptation research 7 and demonstrates a limitation of our search. Nonetheless, a systematic approach was helpful in ensuring our review was transparent and replicable.

The search was conducted in February 2023 and was limited to literature from 2013 to 2023 (inclusive). Most literature on adaptation has been produced within the past fifteen years, such that limiting this search to the past ten years only eliminated 10% of the search results. Earlier literature does introduce the idea of the enablers of climate change adaptation and its theoretical underpinnings, however limiting our search by year helped to ensure the results we reviewed draw on more recent empirical understandings of adaptation and illustrate the current state of knowledge.

Using the filters provided within Scopus, we screened the results by removing keywords related to ‘autonomous’ adaptations within biophysical systems and non-human species, such as ‘genetics’ ‘phylogeny’ ‘acclimation’ or ‘nonhuman’, which are beyond the scope of this study’s focus. With these filters applied, and removing corrections and commentaries, the search produced 320 papers for further review (see Fig. 1 for the selection process as per the Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) guidelines 8 ).

An outline of the systematic review approach that resulted in a total of 144 papers matching inclusion criteria, reported using the PRISMA guidelines 8 .

The titles, keywords, and abstracts for the remaining 320 papers were then screened for eligibility against our criteria for empirical papers that identified enablers, drivers or determinants of adaptation in human systems.

Rejected articles

At total of 202 search results were removed at screening and a further 176 papers were removed after initial review. This included: 114 papers that upon closer reading were not in any way about enablers of adaptation; 33 papers about adaptation in biological systems (see below); 20 papers that were about adaptive capacity and not adaptation practices per se; and 18 papers that were not empirical. We excluded papers that theorise about enablers or investigate adaptive capacity, given the recognition that there is often a significant gap between what is thought to cause adaptation and actual adaptation practice 9 .

Included articles

Over 100 of the papers matching our inclusion criteria investigated drivers or determinants of adaptation in agricultural households (or by agricultural landholders). To avoid skewing results through the experience of this particular sector and set of actors, we chose to review these papers separately and draw on several existing reviews which had previously analysed the findings and methods of these papers (drawn from the existing search results, see Fig. 1 ). The remaining 38 papers that were included describe the enablers of adaptation among various actors working at different scales and sectors, allowing for a clearer analysis of patterns in the research.

Coding and data extraction

The results were coded according to key criteria including research focus, case study location, scale of analysis, and methodology. Qualitative data on the key enablers, determinants or drivers identified in each paper was extracted, analysed, and grouped into common or reoccurring themes.

The literature predominantly consisted of empirical case studies investigating how to enable adaptation at a specific scale, and most often focussing on a specific type of actor (as opposed to networks of actors). Our analysis is therefore coded according to the actors whom the findings primarily apply to. This differentiation is important because it is not always straightforward: for example, Lawrence et al. explore local government adaptation to climate risk by taking into consideration the role of federal and regional governments 10 .

In some cases, articles employed mixed-method approaches to understand enablers of adaptation, which included literature reviews or reviews of adaptation policy in conjunction with empirical data. In these circumstances, the research team only extracted data based on empirical findings. This is similar to the approach of Berrang-Ford et al. who tested whether theorised determinants of adaptive capacity are associated with adaptation policy outcomes 11 .

Drivers of adaptation in agricultural households

Over 70% of the papers matching our inclusion criteria ( n = 105) were investigating the drivers or determinants of adaptation decision-making or outcomes in agricultural households (or by agricultural landholders). Of these papers, 54% are case studies from the Sub-Saharan Africa region, and over 35% are case studies from across Asia (Fig. 2 ). This body of literature has been growing in recent years, with 4 papers published in 2013 and 23 papers published in 2022 (Fig. 3 ). These articles shared similar research approaches and had similar findings, as has been shown in four reviews of this literature 12 , 13 , 14 , 15 .

The geographic regions in which case studies were conducted, for the 105 articles looking at drivers or determinants of agricultural households’ adaptation decision-making, showing a concentration in Sub-Saharan Africa.

The number of papers per year, for the 105 articles looking at drivers or determinants of agricultural households’ adaptation decision-making, showing a gradual increase.

The review of the research on enablers of adaptation among agricultural households by Ajala and Chagwiza classifies the determinants of agricultural household adaptation into socio-economic and demographic factors (i.e. age, gender, literacy levels, household size, wealth), institutional factors (i.e. access to extension services, access to credit facilities, government policies), technological factors (i.e. information on climate, new farming technologies), socio-cultural factors (i.e. shared values) and cognitive factors (i.e. relationship with risk) 13 . Similarly, the review from García de Jalón and colleagues grouped drivers of adaptation into human capital, financial resources, infrastructure and technology, social interaction and governance, food security, dependence on agriculture, and attitudes towards the environment and climate change 14 . These enabling factors were echoed in the papers across Africa 12 , 15 and in other regions 16 .

The importance of knowledge and access to information was particularly emphasised in these studies, as was stakeholder engagement and participatory approaches for successful knowledge integration 12 , 15 . The importance of financial resources was also recognised across the studies: for example, Seidl et al.’s study of irrigators in Australia found financial capital to be the most statistically significant driver of adaptation actions 16 .

Trends in the empirical research from other actors

From here forward, our findings refer only to the 38 papers not focused on agriculture.

There were no clear trends in the date of publication of the 38 remaining articles: the most published in any given year was seven (in 2018), and the least was 2016 (1 paper). A large share (41.6%) of the studies were from journals Scopus categorised as primarily being in the field of environmental studies, such as Current Opinion in Environmental Sustainability or Environmental Science and Policy , closely followed by those identified as being in the social sciences, such as Climate and Development (32.5%). The empirical case studies reviewed were predominantly reporting on cases in Europe, sub-Saharan Africa, Oceania, and North America.

We grouped the enabling factors identified according to common or reoccurring themes, which are discussed in detail below. Figure 4 summarises the enabling factors identified in the literature according to the number of papers in which they were discussed and the main actors in each paper. It shows that there were a disproportionate number of studies focussing on local actors, be they local governments ( n = 15) or local communities ( n = 8). Studies of enablers applied to local communities and local governments both tended to emphasise the importance of leadership and social capital but made little mention of incentives or values.

In contrast, the literature provides little evidence about what enables adaptation at the level of individuals, in the private sector, in regional or provincial levels of government, and among national governments. Though there was mention of factors such as institutional support, risk perception, and trigger events, there was limited empirical evidence to justify that these were important enabling factors for these actors. Moreover, despite some insightful findings, there was no compelling evidence about the importance of some enabling factors, such as values and place attachment 17 , laws, and regulations 18 , or mainstreaming 19 .

Below we summarise the ten most mentioned enabling factors in the 38 articles reviewed (Fig. 4 ). These should not be read as definitive given the number of empirical papers is small and the absence of many studies beyond the local scale (Fig. 4 ), as is discussed further below.

a The number of references to different enabling factors and the actors those factors are primarily applied to (most papers refer to more than one enabler). b The total number of papers for each actor, ordered by scale.

Proactive Leaders

The idea of leadership was widely examined in this literature. In most cases, leadership referred to individuals who champion adaptation and who work to overcome barriers or create enabling conditions 4 , 20 , 21 , 22 . This enabler was particularly prominent in cases of adaptation in local communities and local governments (Fig. 4 ).

It is clear from the literature that government and private sector personnel who are committed, dedicated, and motivated to pursue adaptation in a professional capacity can play a significant role in enabling change 23 , 24 , 25 , 26 . Typically, these individuals understand the importance of climate change, are often involved in climate change research, and notice climate change impacts in their environment 25 , 27 . Such leaders often initiate change by putting in place adaptation policies, strategies and guiding documents, and ensuring these become normalised through their organisations 4 , 28 , 29 , 30 .

Local communities have also been shown to lead adaptation themselves through ‘bottom-up’ approaches 31 , which can achieve outcomes that are better suited to their local context 21 , 32 . Such efforts are even more effective when supported by leaders at higher levels 33 .

Sufficient resourcing

Much of the literature demonstrates the need for financial, human, and natural resources, as well as technology, to enable adaptation 4 , 10 , 20 , 23 , 28 , 34 , 35 . These factors were seen to be particularly important for local governments.

The importance of resources is self-evident, though the discussion tends to focus on the financial resources 23 , 33 , which perhaps reflects the emphasis placed on adaptation funding in the climate change regime, as well as the chronic problem of insufficient funding for local governments in most countries. The literature shows that because finance is so important, those who control its supply have disproportionate power in the adaptation process, often to the detriment of the priorities of lower-level stakeholders 32 , 34 , 36 . There is not only a tendency of donors to ignore local priorities (e.g. as presented by Westoby et al 32 .), but also for international donors to ignore national priorities 34 .

Resources are also shown to matter for the private sector, where actors are of course motivated to pursue climate change adaptation when it delivers economic benefits such as a reduction in costs, increased competitive advantage, or increasing property values 37 , though the literature regarding the private sector is small. The literature also fails to explore the influence of resources on the adaptation behaviour of individuals.

Some studies recognise that sufficient resourcing does not guarantee action on adaptation, let alone effective action. The study by Birchall and colleagues of regional governments reveals that although sufficient resources were guaranteed toward adaptation, conflicting priorities caused momentum to be lost before implementation was complete 28 . This suggests resources are best considered to be important among a larger set of conditions that contribute to an enabling environment for adaptation.

Adaptation knowledge

The literature often demonstrates that knowledge of climate risk and of possible adaptation responses is necessary to enable adaptation across almost all actors 21 , 33 , 37 , 38 , 39 . Considerable focus is placed on how knowledge is transferred into the adaptation process, including by engineers, consultants, extension services, and academics 4 , 10 , 27 . Training courses and other programs that develop the capacity of individuals working on climate change are considered important, as trained people are better equipped to find and handle the information necessary to make informed adaptation decisions 21 , 36 .

Coordination

Often mentioned in the literature about adaptation in governments, horizontal and vertical coordination between and within levels of government has been shown to enable consistent and efficient adaptation action 10 , 22 , 28 , 29 , 40 . The means of such coordination varies, as to be effective it should take into consideration factors including the physical environment, social structure, and local economy, and should be developed to fit the particular context 41 , 42 . In government, defining clear roles and responsibilities for different actors can allow lower levels of government to be more proactive, help share the risks of action and inaction, and promote knowledge sharing 10 , 22 , 29 , 30 , 36 . Conversely, the literature suggests that a lack of communication across levels of government can lead to poor planning decisions or maladaptation 22 .

Institutional support

The literature suggests that adaptation is enabled when the goals, policies and priorities of actors align to support those (leaders) who seek to implement adaptation. This was said to be most important at all levels of government (Fig. 4 ).

Shared goals, policies, and priorities give adaptation practitioners the independence necessary to progress adaptation, and the confidence that they are aligning with mandated priorities 25 , 26 , 28 . A well-integrated mandate for adaptation action within a governing body allows for a gradual increase in investment and capacity development 23 , 34 , 36 . It can also help to streamline the incorporation of adaptation across an organisation and incentivise policy actors to implement adaptation more actively and explicitly 26 , 36 . This is all, however, dependent on the support of elected officials, which in turn hinges on a mandate (or at least not popular opposition) for climate change adaptation. Political stability is also important as it creates a stable operating environment that gives governments the ability to make decisions and see them through 4 , 20 , 23 , 24 .

In Bowen et al.’s study of adaptation in the health sector in Cambodia, interviewees identified the formation of a National Climate Change Committee as the key change that enabled adaptation activities 34 . In this case, the Prime Minister was named Honorary Chair of the committee, which created significant buy-in from diverse actors and meant that higher levels of government had political incentives to commit to adaptation activities 34 .

Risk perception

The literature consistently shows that people, institutions, and organisations who perceive their climate risk to be high are most likely to take action to reduce their vulnerability 20 , 23 , 39 , 41 , 43 , 44 , 45 . Information that increases awareness of climate risks and a sense of urgency to responses can therefore help enable adaptation action 44 . There is also some evidence that those who know and understand the causes and consequences of climate change are more concerned about its potential effects, and so more likely to seek to implement change 38 . Understanding risk can lead to understanding that climate change can result in costly impacts, which can lead to financial arguments in favour of adaptation 4 , 41 , even in the absence of other external motivators 23 . Knowledge of effective adaptation measures can also overcome information barriers, and increase expectation of success, and in these ways helps enable adaptation actions 39 , 44 . Similar to financial resources, the influence of other external factors on these processes is important to consider, as is discussed below in trigger events .

Social capital (Networks)

The literature emphasizes the role of both bonding and bridging networks in enabling adaptation 4 , 21 , 32 , 46 . These social connections were most often discussed in relation to adaptation by local communities and local governments (Fig. 4 ).

Bonding social capital is shown to be important in building community resilience to climate shocks 46 , 47 , 48 . For example, community groups can be important in connecting vulnerable households to the resources and support they need to achieve sustainable adaptation 46 . Bonding social capital also helps foster collective action by increasing participation, cooperation, and problem solving 32 , 48 . Bridging social capital was shown to be important in systems of government, where networked individuals and organisations enable cooperation, knowledge sharing, and skill transfers that help promote adaptation 25 , 35 , 36 , 49 . Partnerships and networks can also help overcome human, financial, and knowledge resource barriers 25 .

Effective communication

Closely related to the issue of consultation or community participation (below), the literature also highlights the need for clear and accessible communication of climate risk and adaptation information in enabling adaptation decisions 10 , 20 , 33 , 38 , 45 , 47 . Communicating information helps to build a mandate for change, alleviate opposition to change, and allows stakeholders to participate and contribute purposefully to adaptation plans 33 , 41 , 42 , 50 .

Participation

Stakeholder participation as an enabler of adaptation is strongly tied to activities conducted by local governments (see Fig. 4 ), which likely reflects their role as key liaison to communities on new initiatives. The literature demonstrates that active engagement of stakeholders in decision-making processes (beyond more basic consultation processes) for adaptation policy and project development can promote the inclusion of different knowledges, perspectives, and experiences 10 , 26 , 32 , 42 , 50 . The evidence demonstrates that local people usually have the best understanding of the adaptation context, are best placed to anticipate and account for unintended effects of adaptation, and devise better responses 20 , 32 , 50 . Engagement can therefore improve the quality of decision-making processes, helping to assure the legitimacy and acceptance of adaptation amongst local communities 50 , or clarify the expectations and objectives of the private sector 37 . Participation in a collaborative and open adaptation process can also build capacity 34 , 35 .

Trigger events

Finally, the literature demonstrates that there are triggering events or windows of opportunity in which the environment is more favourable for the implementation of adaptation 20 , 25 , 27 , 30 . The influence of trigger events was particularly emphasised in reports of local-scale action 20 , 25 . Understanding their influence on private sector and national governments appears to be a significant gap in the literature (Fig. 4 ).

Certain events can trigger a change in the perception of climate risk and the need to adapt, and these most often include focussing events such as extreme weather and disasters but can also include other drivers such as Conferences of Parties to the UNFCCC, increases in funding, or energy crises 23 , 25 , 34 , 43 , 45 . The influence of trigger events is linked to risk perception and the tendency of people to distance themselves from climate risks over time 45 . The literature suggests that trigger events increase the salience and valence of climate risks, and so give leaders a stronger mandate to implement adaptation, innovation, and new communication strategies 4 , 45 . Whether these outcomes can be sustained during recurrent or increasingly severe climate events, political instability or other influential circumstances is, however, important to consider, though the literature reviewed here is not conclusive on this. While trigger events are therefore recognised as important for enabling adaptation, they are not sufficient by themselves 23 , and change is greatly enabled when pre-determined ideas and plans are able to be drawn on at short notice. For example, in their study of adaptation in local government in South Africa, Spires, and Shackleton explore how it was important for the momentum created by certain events to be used to drive the institutionalisation of adaptation and/or long-term interventions rather than allowing reactive responses 25 .

Other enabling factors

Adjacent to the idea of ‘risk perception’, several papers mention that experience with responding to climate variability can positively influence a community or person’s sense of self-efficacy and in turn its propensity to adapt 21 , 34 , 44 , 45 . For example, in their study of fishing communities in North-eastern USA, Maltby et al. note that the community’s historical experiences with adjusting to variability in fish stocks significantly influenced their ability to adapt to new challenges 21 . This suggests that experiential learning plays a role in enabling climate change adaptation and links to additional evidence that was not captured by this review, discussed below.

A number of other important enabling factors were identified in our review of the literature including mainstreaming: the practice of integrating adaptation policies and planning throughout government or business 42 , 51 , laws and regulations: which have the power to both enable and constrain adaptation 37 , 42 and environmental values: which can influence a person to be more amenable to supporting adaptation actions 38 , 39 . The evidence found in this review for these remaining enabling factors was sparse and not sufficient to draw any conclusions.

Interrogating the scope of the literature

It is possible that a proliferation of evidence about the enablers of adaptation comes from research at the local scale because this is where most action happens, which would be consistent with the common understanding that adaptation is a local issue that influences local populations and geographies and requires planning at the local level 4 , 22 , 50 , 52 . Nevertheless, this bias in evidence seems to miss more than it includes given it is also widely understood (and is confirmed by the studies reviewed here) that adaptation is enabled and more effective when it is a collective activity that works across scales and sectors. The relative lack of studies from higher scales and other sectors therefore suggests a need for much more research with non-local government actors, and with civil society and private actors at all scales. Indeed, there are surprisingly few studies focused on not-for-profit or non-government organisations beyond those rooted in local communities 32 . Similarly, it is important to consider the drivers or enablers of individual adaptation actions and the role they may play in generating demand for adaptation policies and projects from the government. It is very likely that more detail on factors enabling adaptation for these actors, as well as national governments, could be found in grey literature case studies which were not reviewed in this paper.

Limitations to our approach may also have influenced this evidence about the enablers of adaptation, and the distribution shown in Fig. 4 . In using Scopus we no doubt excluded articles from journals not listed in Scopus, which may explain the lack of literature from law, medical, or health journals. Thus it is likely that laws and regulations as enablers have been explained more than is been represented in our study. It is also possible that our use of keywords omitted some insights on enablers from environmental conservation and biological fields of study.

Given the overlap of research on adaptation with other disciplines, future work should seek to capture a wider body of literature from databases such as PubMed, and from those that better capture grey literature (such as Google Scholar). This is especially important for some fields such as law and health sciences which tend to have their own bespoke databases, and capture outputs produced by non-profit organisations, national governments, and the private sector. Alternate methodologies such as scoping review could also be used to identify relevant papers that use different language or keywords to discuss factors important to enabling adaptation, such as the paper by Porter et al. discussing the importance of high-level political support 53 or work on the importance of experiential learning by Baird et al. among others 54 , 55 . Finally, the link between adaptive capacity and actual adaptation implementation has not been well represented here and could be a focus of future investigations.

Considering barriers and enablers

As their counterpart, several papers take the approach of identifying enabling factors and barriers concurrently 20 , 25 , 30 , and enabling factors are sometimes posed as the opposite of the well-researched barriers to adaptation. While there is undoubtedly a strong correlation between enabling factors and barriers to adaptation, our review suggests that enabling factors are not independent of one another and may not directly remove barriers. Instead, the existing literature suggests that to promote adaptation a combination of enabling conditions must be facilitated to create an enabling environment. This was demonstrated, for example, by Birchall and colleagues highlighting the need for other enablers alongside financial resources 28 .

While our approach of grouping the literature helps to demonstrate that there are likely many combinations of associations between enabling factors and actors that mutually enable change, it was not able to fully capture these connections or highlight which factors are most influential, given the still small number of empirical studies from which to learn. These processes were explained well in two papers in particular 10 , 22 . Further work to translate this knowledge of enabling factors into a tangible and accessible resource of benefit to different actors would require frameworks or models that show how these sequencing of factors can affect change, as has been done extensively regarding barriers 5 , 6 or when developing decision making frameworks 56 .

Understanding of how adaptation is enabled is constrained by the relatively small number of empirical studies that explain actual instances of adaptation. Our review finds that some factors seem to be more important than others, including resources (and especially money), knowledge of climate risks and responses, leadership, social capital, and the support of institutions in which adaptation actors are nested. Together, the literature suggests that to promote adaptation a combination of different enabling factors is necessary to create an enabling environment amenable to change. These findings have explanatory power when applied to adaptation at local and household levels, which is the focus of much of the research. There is a need, however, for further research that can explain the factors and processes that enable adaptation in institutions that are not ‘local’, in regional/provincial and national governments, in the private sector, and non-local not-for profit and non-governmental organisations.

Data availability

The authors confirm that all data generated or analysed during this study are included in this published article.

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Acknowledgements

We would like to acknowledge the financial support from the National Environmental Science Program (NESP) Climate Systems Hub for conducting this review as part of the project Enabling Best Practice Adaptation .

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Brullo, T., Barnett, J., Waters, E. et al. The enablers of adaptation: A systematic review. npj Clim. Action 3 , 40 (2024). https://doi.org/10.1038/s44168-024-00128-y

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Design with care : We take precautions at every stage of our process, including design, model training, feature development, and quality evaluation to identify how our AI tools may be misused or lead to potential harm. We will continuously and proactively improve our AI tools with the help of user feedback.
Protect privacy : We protect our users' privacy with powerful on-device processing and groundbreaking infrastructure like Private Cloud Compute. We do not use our users' private personal data or user interactions when training our foundation models.

These principles are reflected throughout the architecture that enables Apple Intelligence, connects features and tools with specialized models, and scans inputs and outputs to provide each feature with the information needed to function responsibly.

In the remainder of this overview, we provide details on decisions such as: how we develop models that are highly capable, fast, and power-efficient; how we approach training these models; how our adapters are fine-tuned for specific user needs; and how we evaluate model performance for both helpfulness and unintended harm.

Pre-Training

Our foundation models are trained on Apple's AXLearn framework , an open-source project we released in 2023. It builds on top of JAX and XLA, and allows us to train the models with high efficiency and scalability on various training hardware and cloud platforms, including TPUs and both cloud and on-premise GPUs. We used a combination of data parallelism, tensor parallelism, sequence parallelism, and Fully Sharded Data Parallel (FSDP) to scale training along multiple dimensions such as data, model, and sequence length.

We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control.

We never use our users’ private personal data or user interactions when training our foundation models, and we apply filters to remove personally identifiable information like social security and credit card numbers that are publicly available on the Internet. We also filter profanity and other low-quality content to prevent its inclusion in the training corpus. In addition to filtering, we perform data extraction, deduplication, and the application of a model-based classifier to identify high quality documents.

Post-Training

We find that data quality is essential to model success, so we utilize a hybrid data strategy in our training pipeline, incorporating both human-annotated and synthetic data, and conduct thorough data curation and filtering procedures. We have developed two novel algorithms in post-training: (1) a rejection sampling fine-tuning algorithm with teacher committee, and (2) a reinforcement learning from human feedback (RLHF) algorithm with mirror descent policy optimization and a leave-one-out advantage estimator. We find that these two algorithms lead to significant improvement in the model’s instruction-following quality.

Optimization

In addition to ensuring our generative models are highly capable, we have used a range of innovative techniques to optimize them on-device and on our private cloud for speed and efficiency. We have applied an extensive set of optimizations for both first token and extended token inference performance.

Both the on-device and server models use grouped-query-attention. We use shared input and output vocab embedding tables to reduce memory requirements and inference cost. These shared embedding tensors are mapped without duplications. The on-device model uses a vocab size of 49K, while the server model uses a vocab size of 100K, which includes additional language and technical tokens.

For on-device inference, we use low-bit palletization, a critical optimization technique that achieves the necessary memory, power, and performance requirements. To maintain model quality, we developed a new framework using LoRA adapters that incorporates a mixed 2-bit and 4-bit configuration strategy — averaging 3.5 bits-per-weight — to achieve the same accuracy as the uncompressed models.

Additionally, we use an interactive model latency and power analysis tool, Talaria , to better guide the bit rate selection for each operation. We also utilize activation quantization and embedding quantization, and have developed an approach to enable efficient Key-Value (KV) cache update on our neural engines.

With this set of optimizations, on iPhone 15 Pro we are able to reach time-to-first-token latency of about 0.6 millisecond per prompt token, and a generation rate of 30 tokens per second. Notably, this performance is attained before employing token speculation techniques, from which we see further enhancement on the token generation rate.

Model Adaptation

Our foundation models are fine-tuned for users’ everyday activities, and can dynamically specialize themselves on-the-fly for the task at hand. We utilize adapters, small neural network modules that can be plugged into various layers of the pre-trained model, to fine-tune our models for specific tasks. For our models we adapt the attention matrices, the attention projection matrix, and the fully connected layers in the point-wise feedforward networks for a suitable set of the decoding layers of the transformer architecture.

By fine-tuning only the adapter layers, the original parameters of the base pre-trained model remain unchanged, preserving the general knowledge of the model while tailoring the adapter layers to support specific tasks.

We represent the values of the adapter parameters using 16 bits, and for the ~3 billion parameter on-device model, the parameters for a rank 16 adapter typically require 10s of megabytes. The adapter models can be dynamically loaded, temporarily cached in memory, and swapped — giving our foundation model the ability to specialize itself on the fly for the task at hand while efficiently managing memory and guaranteeing the operating system's responsiveness.

To facilitate the training of the adapters, we created an efficient infrastructure that allows us to rapidly retrain, test, and deploy adapters when either the base model or the training data gets updated. The adapter parameters are initialized using the accuracy-recovery adapter introduced in the Optimization section.

Performance and Evaluation

Our focus is on delivering generative models that can enable users to communicate, work, express themselves, and get things done across their Apple products. When benchmarking our models, we focus on human evaluation as we find that these results are highly correlated to user experience in our products. We conducted performance evaluations on both feature-specific adapters and the foundation models.

To illustrate our approach, we look at how we evaluated our adapter for summarization. As product requirements for summaries of emails and notifications differ in subtle but important ways, we fine-tune accuracy-recovery low-rank (LoRA) adapters on top of the palletized model to meet these specific requirements. Our training data is based on synthetic summaries generated from bigger server models, filtered by a rejection sampling strategy that keeps only the high quality summaries.

To evaluate the product-specific summarization, we use a set of 750 responses carefully sampled for each use case. These evaluation datasets emphasize a diverse set of inputs that our product features are likely to face in production, and include a stratified mixture of single and stacked documents of varying content types and lengths. As product features, it was important to evaluate performance against datasets that are representative of real use cases. We find that our models with adapters generate better summaries than a comparable model.

As part of responsible development, we identified and evaluated specific risks inherent to summarization. For example, summaries occasionally remove important nuance or other details in ways that are undesirable. However, we found that the summarization adapter did not amplify sensitive content in over 99% of targeted adversarial examples. We continue to adversarially probe to identify unknown harms and expand our evaluations to help guide further improvements.

In addition to evaluating feature specific performance powered by foundation models and adapters, we evaluate both the on-device and server-based models’ general capabilities. We utilize a comprehensive evaluation set of real-world prompts to test the general model capabilities. These prompts are diverse across different difficulty levels and cover major categories such as brainstorming, classification, closed question answering, coding, extraction, mathematical reasoning, open question answering, rewriting, safety, summarization, and writing.

We compare our models with both open-source models (Phi-3, Gemma, Mistral, DBRX) and commercial models of comparable size (GPT-3.5-Turbo, GPT-4-Turbo) 1 . We find that our models are preferred by human graders over most comparable competitor models. On this benchmark, our on-device model, with ~3B parameters, outperforms larger models including Phi-3-mini, Mistral-7B, and Gemma-7B. Our server model compares favorably to DBRX-Instruct, Mixtral-8x22B, and GPT-3.5-Turbo while being highly efficient.

We use a set of diverse adversarial prompts to test the model performance on harmful content, sensitive topics, and factuality. We measure the violation rates of each model as evaluated by human graders on this evaluation set, with a lower number being desirable. Both the on-device and server models are robust when faced with adversarial prompts, achieving violation rates lower than open-source and commercial models.

Our models are preferred by human graders as safe and helpful over competitor models for these prompts. However, considering the broad capabilities of large language models, we understand the limitation of our safety benchmark. We are actively conducting both manual and automatic red-teaming with internal and external teams to continue evaluating our models' safety.

To further evaluate our models, we use the Instruction-Following Eval (IFEval) benchmark to compare their instruction-following capabilities with models of comparable size. The results suggest that both our on-device and server model follow detailed instructions better than the open-source and commercial models of comparable size.

We evaluate our models’ writing ability on our internal summarization and composition benchmarks, consisting of a variety of writing instructions. These results do not refer to our feature-specific adapter for summarization (seen in Figure 3 ), nor do we have an adapter focused on composition.

The Apple foundation models and adapters introduced at WWDC24 underlie Apple Intelligence, the new personal intelligence system that is integrated deeply into iPhone, iPad, and Mac, and enables powerful capabilities across language, images, actions, and personal context. Our models have been created with the purpose of helping users do everyday activities across their Apple products, and developed responsibly at every stage and guided by Apple’s core values. We look forward to sharing more information soon on our broader family of generative models, including language, diffusion, and coding models.

[1] We compared against the following model versions: gpt-3.5-turbo-0125, gpt-4-0125-preview, Phi-3-mini-4k-instruct, Mistral-7B-Instruct-v0.2, Mixtral-8x22B-Instruct-v0.1, Gemma-1.1-2B, and Gemma-1.1-7B. The open-source and Apple models are evaluated in bfloat16 precision.

Apple Natural Language Understanding Workshop 2023

Earlier this year, Apple hosted the Natural Language Understanding workshop. This two-day hybrid event brought together Apple and members of the academic research community for talks and discussions on the state of the art in natural language understanding.

In this post, we share highlights from workshop discussions and recordings of select workshop talks.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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Title: qlora: efficient finetuning of quantized llms.

Abstract: We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) double quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) paged optimziers to manage memory spikes. We use QLoRA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training.

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IMAGES

(PDF) The Relationship between Pain Experience and Roy Adaption Model
Quality of Life Through the Prism of the Roy Adaptation Model
(PDF) Nursing Theory-Guided Practice: The Roy Adaptation Model and
(PDF) Using the Roy Adaptation Model to Guide Research and/or Practice
(PDF) Implementing the Roy Adaptation Model: From Theory to Practice
Table 1 from An exploration of Roy's Adaptation Model and the process

VIDEO

21. Form Models III and IV: Rationality and Memory
Callista Roy "Adaptation Model in Practice" 1993
Lecture 15- Pride and Prejudice IV: Theme, Form & Style
Callista Roy 1989
⭕7th std science|CH-1|The Living world -Adaptation & classification|Introduction part-1|(with notes
Lecture 16- Pride and Prejudice V: Theme, Form & Style

COMMENTS

Application of the Roy Adaptation Theory to a care program for nurses
The Roy Adaptation Theory may serve as a guide in the development and evaluation of a hospital-based program designed to support the needs of the healthcare team. This discussion will explore the application of the Roy Adaptation Theory-Group Identity Mode to the Tea for the Soul Care Model for nurses. ... Journal of Research in Nursing. 2017: ...
The Roy Adaptation Model: A Theoretical Framework for Nurses Providing
Over the past 50 years, the Roy Adaptation Model (RAM) has been used to guide interdisciplinary education, knowledge development, practice, and research. 1-2 The purpose of this paper is to provide an overview of the RAM as a theoretical framework to better understand individuals with anorexia nervosa (AN) as they experience weight restoration ...
Conceptualisation and measurement of adaptation within the Roy
In this paper, we formulate a protocol for a scoping review to provide an overview of the RAM as a conceptual model for chronic care. ... Boston Based Adaptation Research in Nursing Society Roy adaptation model-based research: 25 years of contribution to nursing science. Indianapolis Center Nursing Press, 1999. [Google Scholar] 17. Roy C ...
(PDF) The Roy adaptation model and research
The four adaptative modes defined in the Roy Adaptation Model are physiological, role function, self-concept, and interdependence modes (Roy, 2009;Vicdan & Karabacak, 2016). Roy determined basic ...
Quality of Life Through the Prism of the Roy Adaptation Model
The concept of quality of life (QOL) is considered from the perspective of the Roy adaptation model (RAM; Roy, 2009). Roy's (2009) theoretical model is made up of four adaptive modes that include (a) physiologic-physical, (b) self-concept/group identity, (c) role function, and (d) interdependence. The adaptation process includes input of stimuli and flow through the coping processes, and it ...
Research Based on the Roy Adaptation Model: Last 25 Years
Abstract. Two key events lead to the prominence of links among Roy's grand theory, derived middle-range theories and the design of research. The author in this column describes this work in two formats. Essential details of two areas of research are presented in episodic form—the first is work on secondary analysis of Roy model-based ...
Callista Roy's Adaptation Model
Roy adaptation model, Roy. The work of Sr. Callista Roy covers over 40 years. From the early development of the model through the application of the model to nursing education, practice, and research, Roy and the adaptation model have touched the lives of many, personally, professionally, intellectually, and spiritually (Roy, 2009).
PDF Roy's adaptation model: principles and general applications
The Roy adaptation model is clinically applicable because it provides a nursing framework to evaluate behaviour and structure appropriate inter vention. Goal-setting is also facilitated. The theory analysis identified by Dickoff and James (1968) provides a framework for analysis of the elements of Roy's adaptation model: 1.
Review of Roy adaptation model-based qualitative research
Abstract. The aim of this paper is to examine the role of qualitative research in the development of the Roy adaptation model. An exploration of the findings from qualitative research using Roy's adaptation model from 1995 to 2005 is compared with the findings and recommendations from a previous review over a 25-year period (1970-1995).
Theory Guided Practices: An Approach to Better Nursing Care through Roy
Aim: The paper aims to elaborate on the theory-guided nursing practice by utilization of the Roy Adaptation Model in the clinical. setup for delivering effective nursing care. Methodology: This ...
The Roy adaptation model and research
The Roy adaptation model and research. The Roy adaptation model and research Nurs Sci Q. 2009 Jul;22(3):209-11. doi: 10.1177/0894318409338692. Authors Callista Roy 1 , Martha V Whetsell, Keville Frederickson. Affiliation 1 Boston College. PMID: 19694080 DOI: 10. ...
Roy Adaptation Model: Theory-Based Knowledge and Nursing Care With a
Abstract. Nursing theories shed light and guide nursing care through provision of care to persons based on the specialized knowledge base of the profession. Nurses utilizing Roy's adaptation model deliver holistic care by accounting for people, processes, and the environments. The aim of this article is to illustrate the value of utilizing the ...
Review of Roy Adaptation Model-Based Qualitative Research
An exploration of the findings from qualitative research using Roy's adaptation model from 1995 to 2005 is compared with the findings and recommendations from a previous review over a 25-year period (1970-1995). The aim of this paper is to examine the role of qualitative research in the development of the Roy adaptation model. An exploration of the findings from qualitative research using Roy ...
The Roy Adaptation Model and Research
This chapter focuses on the relationship between coping strategies and impaired ADL in elderly stroke patients as the first report of Roy's (2007) Coping and Adaptation Processing Scale (CAPS) Japanese version. Roy (2009) highlighted that, as members of a profession, nurses use specialized knowledge to contribute to the needs of society for health and well-being. The Roy adaptation model ...
Using the Roy Adaptation Model to Guide Research and/or Practice
An explanation of a three-step process for the use of a conceptual model to guide nursing research and/or practice to one conceptual model of nursing: the Roy Adaptation Model is presented. This paper presents an explanation of a three-step process for the use of a conceptual model to guide nursing research and/or practice. Step 1 involves learning the content of the conceptual model and its ...
The Roy Adaptation Model and Research: Global Perspective
The Roy Adaptation Model and Research: Global Perspective. Callista Roy, RN; ... The application of the Roy adaptation model in Japanese stroke patients. Paper presented at the Roy Adaptation Conference, Mount St. Mary's College, Los Angeles. Google Scholar. Roy, C. (2007). Coping and adaptation processing coping scale (CAPS). Boston: Boston ...
Implementing the Roy Adaptation Model: From Theory to Practice
Frederickson, K. (1991). Nursing Theories-A Basis for Differentiated Practice: Application of the Roy Adaptation Model in Nursing Practice. Paper presented at the Proceeding of Annual Meeting Differentiating Nursing Practice into the Twenty-first Century. Frederickson, K. (1993). Translating the Roy Adaptation Model into Practice and Research.
nursing theory: Roy Adaptation Model Research Papers
Roy Adaptation Model has the five main concepts of nursing theory: the health, the person, the nurse, the adaptation and the environment. Roy views the person in a holistic way. The core concept in her model is adaptation. The concept of adaptation assumes that a person is an open system who responds to stimuli
Nursing Approach Based on Roy Adaptation Model in a Patient Undergoing
Conceptual Framework: Roy Adaptation Model. The Roy Adaptation Model is a model widely used in the identification of conceptual foundation of nursing. The development of the model began in the late 1960s . Nurses from the United States and countries all around the world aim to emphasize and explain RAM as well as expanding its concepts .
A Critical Analysis of Using Roy'S Adaptation Model in Nursing Research
Key words: Roy's Adaptation Model, Nursing, Stress. 1. INTRODUCTION Roy's Adaptation Model (RAM) is one of the most useful conceptual frameworks that guides nursing practice, directs research and influences education (1). It focuses on individuals' adaptation to changeable environment and guides the assessment of individuals' adaptation.
The Roy Adaptation Model: a consideration of its properties as a
The underlying premise of this paper is the belief that one of the core values of nursing research is its ability to demonstrate that nursing interventions influence care outcomes. From this perspective the contribution offered by the Roy Adaptation Model for Nursing within an intervention study is …
Roy Adaptation Model
The Roy Adaptation Model (RAM), see Figure 1, is a well well-known known theoretical framework for designing nursing intervention for patients (Rice, 2011). . Under this framework, the patient is ...
The enablers of adaptation: A systematic review
The review of the research on enablers of adaptation among agricultural households by Ajala and Chagwiza classifies the determinants of agricultural household adaptation into socio-economic and ...
[PDF] Roy Adaptation Model
A person's ability to respond positively to environmental changes, the person must adapt and has 4 modes of adaptation: physiologic needs, self-concept, role function and interdependence. Assumptions • The person is a bio-psycho-social being. The person is in constant interaction with a changing environment. • To cope with a changing world, person uses both innate and acquired mechanisms ...
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Roy’s adaptation model: principles and general applications

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Nursing Approach Based on Roy Adaptation Model in a Patient Undergoing Breast Conserving Surgery for Breast Cancer

Özgül Karayurt

Özge İşeri

Introduction

Conceptual Framework: Roy Adaptation Model

The Physiologic Mode

The Self Concept Mode

The Role Function Mode

The Interdependence Mode

Nursing approach based on Roy Adaptation Model in a patient undergoing breast conserving surgery

A CRITICAL ANALYSIS OF USING ROY’S ADAPTATION MODEL IN NURSING RESEARCH

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The Roy Adaptation Model: a consideration of its properties as a conceptual framework for an intervention study

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The enablers of adaptation: A systematic review

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Coding and data extraction

Drivers of adaptation in agricultural households

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Proactive Leaders

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Risk perception

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Interrogating the scope of the literature

Considering barriers and enablers

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Roy Adaptation Model

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