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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

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

Importance of a Good Results Section

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

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

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

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

Structure and Writing Style

I.  Organization and Approach

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

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

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

II.  Content

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

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

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

III.  Problems to Avoid

When writing the results section, avoid doing the following :

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

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

Writing Tip

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

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

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

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Findings refer to the results obtained from analyzing collected data during a research study. They provide answers to the research questions posed at the beginning of the investigation.

Related terms

Conclusion : A conclusion is a summary of the main findings and interpretations drawn from the research study.

Data Analysis : Data analysis involves organizing, interpreting, and drawing conclusions from collected data using various statistical or qualitative techniques.

Implications : Implications are the potential consequences or applications of the research findings for theory, practice, or future studies.

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Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

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Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

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Disseminating research findings: what should researchers do? A systematic scoping review of conceptual frameworks

Paul m wilson.

1 Centre for Reviews and Dissemination, University of York, YO10 5DD, UK

Mark Petticrew

2 Social and Environmental Health Department, London School of Hygiene and Tropical Medicine, WC1E 7HT, UK

Mike W Calnan

3 School of Social Policy, Sociology and Social Research, University of Kent, CT2 7NF, UK

Irwin Nazareth

4 MRC General Practice Research Framework, University College London, NW1 2ND, UK

Associated Data

Addressing deficiencies in the dissemination and transfer of research-based knowledge into routine clinical practice is high on the policy agenda both in the UK and internationally.

However, there is lack of clarity between funding agencies as to what represents dissemination. Moreover, the expectations and guidance provided to researchers vary from one agency to another. Against this background, we performed a systematic scoping to identify and describe any conceptual/organising frameworks that could be used by researchers to guide their dissemination activity.

We searched twelve electronic databases (including MEDLINE, EMBASE, CINAHL, and PsycINFO), the reference lists of included studies and of individual funding agency websites to identify potential studies for inclusion. To be included, papers had to present an explicit framework or plan either designed for use by researchers or that could be used to guide dissemination activity. Papers which mentioned dissemination (but did not provide any detail) in the context of a wider knowledge translation framework, were excluded. References were screened independently by at least two reviewers; disagreements were resolved by discussion. For each included paper, the source, the date of publication, a description of the main elements of the framework, and whether there was any implicit/explicit reference to theory were extracted. A narrative synthesis was undertaken.

Thirty-three frameworks met our inclusion criteria, 20 of which were designed to be used by researchers to guide their dissemination activities. Twenty-eight included frameworks were underpinned at least in part by one or more of three different theoretical approaches, namely persuasive communication, diffusion of innovations theory, and social marketing.

Conclusions

There are currently a number of theoretically-informed frameworks available to researchers that can be used to help guide their dissemination planning and activity. Given the current emphasis on enhancing the uptake of knowledge about the effects of interventions into routine practice, funders could consider encouraging researchers to adopt a theoretically-informed approach to their research dissemination.

Healthcare resources are finite, so it is imperative that the delivery of high-quality healthcare is ensured through the successful implementation of cost-effective health technologies. However, there is growing recognition that the full potential for research evidence to improve practice in healthcare settings, either in relation to clinical practice or to managerial practice and decision making, is not yet realised. Addressing deficiencies in the dissemination and transfer of research-based knowledge to routine clinical practice is high on the policy agenda both in the UK [ 1 - 5 ] and internationally [ 6 ].

As interest in the research to practice gap has increased, so too has the terminology used to describe the approaches employed [ 7 , 8 ]. Diffusion, dissemination, implementation, knowledge transfer, knowledge mobilisation, linkage and exchange, and research into practice are all being used to describe overlapping and interrelated concepts and practices. In this review, we have used the term dissemination, which we view as a key element in the research to practice (knowledge translation) continuum. We define dissemination as a planned process that involves consideration of target audiences and the settings in which research findings are to be received and, where appropriate, communicating and interacting with wider policy and health service audiences in ways that will facilitate research uptake in decision-making processes and practice.

Most applied health research funding agencies expect and demand some commitment or effort on the part of grant holders to disseminate the findings of their research. However, there does appear to be a lack of clarity between funding agencies as to what represents dissemination [ 9 ]. Moreover, although most consider dissemination to be a shared responsibility between those funding and those conducting the research, the expectations on and guidance provided to researchers vary from one agency to another [ 9 ].

We have previously highlighted the need for researchers to consider carefully the costs and benefits of dissemination and have raised concerns about the nature and variation in type of guidance issued by funding bodies to their grant holders and applicants [ 10 ]. Against this background, we have performed a systematic scoping review with the following two aims: to identify and describe any conceptual/organising frameworks designed to be used by researchers to guide their dissemination activities; and to identify and describe any conceptual/organising frameworks relating to knowledge translation continuum that provide enough detail on the dissemination elements that researchers could use it to guide their dissemination activities.

The following databases were searched to identify potential studies for inclusion: MEDLINE and MEDLINE In-Process and Other Non-Indexed Citations (1950 to June 2010); EMBASE (1980 to June 2010); CINAHL (1981 to June 2010); PsycINFO (1806 to June 2010); EconLit (1969 to June 2010); Social Services Abstracts (1979 to June 2010); Social Policy and Practice (1890 to June 2010); Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, Database of Abstracts of Reviews of Effects, Health Technology Assessment Database, NHS Economic Evaluation Database (Cochrane Library 2010: Issue 1).

The search terms were identified through discussion by the research team, by scanning background literature, and by browsing database thesauri. There were no methodological, language, or date restrictions. Details of the database specific search strategies are presented Additional File 1 , Appendix 1.

Citation searches of five articles [ 11 - 15 ] identified prior to the database searches were performed in Science Citation Index (Web of Science), MEDLINE (OvidSP), and Google Scholar (February 2009).

As this review was undertaken as part of a wider project aiming to assess the dissemination activity of UK applied and public health researchers [ 16 ], we searched the websites of 10 major UK funders of health services and public health research. These were the British Heart Foundation, Cancer Research UK, the Chief Scientist Office, the Department of Health Policy Research Programme, the Economic and Social Research Council (ESRC), the Joseph Rowntree Foundation, the Medical Research Council (MRC), the NIHR Health Technology Assessment Programme, the NIHR Service Delivery and Organisation Programme and the Wellcome Trust. We aimed to identify any dissemination/communication frameworks, guides, or plans that were available to grant applicants or holders.

We also interrogated the websites of four key agencies with an established record in the field of dissemination and knowledge transfer. These were the Agency for Healthcare Research and Quality ( AHRQ ) , the Canadian Institutes of Health Research (CIHR), the Canadian Health Services Research Foundation (CHSRF), and the Centre for Reviews and Dissemination (CRD).

As a number of databases and websites were searched, some degree of duplication resulted. In order to manage this issue, the titles and abstracts of records were downloaded and imported into EndNote bibliographic software, and duplicate records removed.

References were screened independently by two reviewers; those studies that did not meet the inclusion criteria were excluded. Where it was not possible to exclude articles based on title and abstract alone, full text versions were obtained and their eligibility was assessed independently by two reviewers. Where disagreements occurred, the opinion of a third reviewer was sought and resolved by discussion and arbitration by a third reviewer.

To be eligible for inclusion, papers needed to either present an explicit framework or plan designed to be used by a researcher to guide their dissemination activity, or an explicit framework or plan that referred to dissemination in the context of a wider knowledge translation framework but that provided enough detail on the dissemination elements that a researcher could then use it. Papers that referred to dissemination in the context of a wider knowledge translation framework, but that did not describe in any detail those process elements relating to dissemination were excluded from the review. A list of excluded papers is included in Additional File 2 , Appendix 2.

For each included paper we recorded the publication date, a description of the main elements of the framework, whether there was any reference to other included studies, and whether there was an explicit theoretical basis to the framework. Included papers that did not make an explicit reference to an underlying theory were re-examined to determine whether any implicit use of theory could be identified. This entailed scrutinising the references and assessing whether any elements from theories identified in other papers were represented in the text. Data from each paper meeting the inclusion criteria were extracted by one researcher and independently checked for accuracy by a second.

A narrative synthesis [ 17 ] of included frameworks was undertaken to present the implicit and explicit theoretical basis of included frameworks and to explore any relationships between them.

Our searches identified 6,813 potentially relevant references (see Figure ​ Figure1). 1 ). Following review of the titles and abstracts, we retrieved 122 full papers for a more detailed screening. From these, we included 33 frameworks (reported in 44 papers) Publications that did not meet our inclusion criteria are listed in Additional File 2 , Appendix 2.

Identification of conceptual frameworks .

Characteristics of conceptual frameworks designed to be used by researchers

Table ​ Table1 1 summarises in chronological order, twenty conceptual frameworks designed for use by researchers [ 11 , 14 , 15 , 18 - 34 ]. Where we have described elements of frameworks that have been reported across multiple publications, these are referenced in the Table.

Conceptual frameworks designed for use by researchers

Theoretical underpinnings of dissemination frameworks

Thirteen of the twenty included dissemination frameworks were either explicitly or implicitly judged to be based on the Persuasive Communication Matrix [ 35 , 36 ]. Originally derived from a review of the literature of persuasion which sought to operationalise Lasswell's seminal description of persuasive communications as being about 'Who says what in which channel to whom with what effect' [ 37 ]. McGuire argued that there are five variables that influence the impact of persuasive communications. These are the source of communication, the message to be communicated, the channels of communication, the characteristics of the audience (receiver), and the setting (destination) in which the communication is received.

Included frameworks were judged to encompass either three [ 21 , 27 , 29 ], four [ 15 , 20 , 23 , 26 , 28 , 31 , 38 ], or all five [ 11 , 18 , 25 ] of McGuire's five input variables, namely, the source, channel, message, audience, and setting. The earliest conceptual model included in the review explicitly applied McGuire's five input variables to the dissemination of medical technology assessments [ 11 ]. Only one other framework (in its most recent version) explicitly acknowledges McGuire [ 17 ]; the original version acknowledged the influence of Winkler et al . on its approach to conceptualising systematic review dissemination [ 18 ]. The original version of the CRD approach [ 18 , 39 ] is itself referred to by two of the other eight frameworks [ 20 , 23 ]

Diffusion of Innovations theory [ 40 , 41 ] is explicitly cited by eight of the dissemination frameworks [ 11 , 17 , 19 , 22 , 24 , 28 , 29 , 34 ]. Diffusion of Innovations offers a theory of how, why, and at what rate practices or innovations spread through defined populations and social systems. The theory proposes that there are intrinsic characteristics of new ideas or innovations that determine their rate of adoption, and that actual uptake occurs over time via a five-phase innovation-decision process (knowledge, persuasion, decision, implementation, and confirmation). The included frameworks are focussed on the knowledge and persuasion stages of the innovation-decision process.

Two of the included dissemination frameworks make reference to Social Marketing [ 42 ]. One briefly discusses the potential application of social and commercial marketing and advertising principles and strategies in the promotion of non-commercial services, ideas, or research-based knowledge [ 22 ]. The other briefly argues that a social marketing approach could take into account a planning process involving 'consumer' oriented research, objective setting, identification of barriers, strategies, and new formats [ 30 ]. However, this framework itself does not represent a comprehensive application of social marketing theory and principles, and instead highlights five factors that are focussed around formatting evidence-based information so that it is clear and appealing by defined target audiences.

Three other distinct dissemination frameworks were included, two of which are based on literature reviews and researcher experience [ 14 , 32 ]. The first framework takes a novel question-based approach and aims to increase researchers' awareness of the type of context information that might prove useful when disseminating knowledge to target audiences [ 14 ]. The second framework presents a model that can be used to identify barriers and facilitators and to design interventions to aid the transfer and utilization of research knowledge [ 32 ]. The final framework is derived from Two Communities Theory [ 43 ] and proposes pragmatic strategies for communicating across conflicting cultures research and policy; it suggests a shift away from simple one-way communication of research to researchers developing collaborative relationships with policy makers [ 33 ].

Characteristics of conceptual frameworks relating to knowledge translation that could be used by researchers to guide their dissemination activities

Table ​ Table2 2 summarises in chronological order the dissemination elements of 13 conceptual frameworks relating to knowledge translation that could be used by researchers to guide their dissemination activities [ 13 , 44 - 55 ].

Conceptual frameworks relating to knowledge translation that could be used by researchers to guide their dissemination activities

Only two of the included knowledge translation frameworks were judged to encompass four of McGuire's five variables for persuasive communications [ 45 , 47 ]. One framework [ 45 ] explicitly attributes these variables as being derived from Winkler et al [ 11 ]. The other [ 47 ] refers to strong direct evidence but does not refer to McGuire or any of the other included frameworks.

Diffusion of Innovations theory [ 40 , 41 ] is explicitly cited in eight of the included knowledge translation frameworks [ 13 , 45 - 49 , 52 , 56 ]. Of these, two represent attempts to operationalise and apply the theory, one in the context of evidence-based decision making and practice [ 13 ], and the other to examine how innovations in organisation and delivery of health services spread and are sustained in health service organisations [ 47 , 57 ]. The other frameworks are exclusively based on the theory and are focussed instead on strategies to accelerate the uptake of evidence-based knowledge and or interventions

Two of the included knowledge translation frameworks [ 50 , 53 ] are explicitly based on resource or knowledge-based Theory of the Firm [ 58 , 59 ]. Both frameworks propose that successful knowledge transfer (or competitive advantage) is determined by the type of knowledge to be transferred as well as by the development and deployment of appropriate skills and infrastructure at an organisational level.

Two of the included knowledge translation frameworks purport to be based upon a range of theoretical perspectives. The Coordinated Implementation model is derived from a range of sources, including theories of social influence on attitude change, the Diffusion of Innovations, adult learning, and social marketing [ 45 ]. The Practical, Robust Implementation and Sustainability Model was developed using concepts from Diffusion of Innovations, social ecology, as well as the health promotion, quality improvement, and implementation literature [ 52 ].

Three other distinct knowledge translation frameworks were included, all of which are based on a combination of literature reviews and researcher experience [ 44 , 51 , 54 ].

Conceptual frameworks provided by UK funders

Of the websites of the 10 UK funders of health services and public health research, only the ESRC made a dissemination framework available to grant applicants or holders (see Table ​ Table1) 1 ) [ 26 ]. A summary version of another included framework is available via the publications section of the Joseph Rowntree Foundation [ 60 ]. However, no reference is made to it in the submission guidance they make available to research applicants.

All of the UK funding bodies made brief references to dissemination in their research grant application guides. These would simply ask applicants to briefly indicate how findings arising from the research will be disseminated (often stating that this should be other than via publication in peer-reviewed journals) so as to promote or facilitate take up by users in the health services.

This systematic scoping review presents to our knowledge the most comprehensive overview of conceptual/organising frameworks relating to research dissemination. Thirty-three frameworks met our inclusion criteria, 20 of which were designed to be used by researchers to guide their dissemination activities. Twenty-eight included frameworks that were underpinned at least in part by one or more of three different theoretical approaches, namely persuasive communication, diffusion of innovations theory, and social marketing.

Our search strategy was deliberately broad, and we searched a number of relevant databases and other sources with no language or publication status restrictions, reducing the chance that some relevant studies were excluded from the review and of publication or language bias. However, we restricted our searches to health and social science databases, and it is possible that searches targeting for example the management or marketing literature may have revealed additional frameworks. In addition, this review was undertaken as part of a project assessing UK research dissemination, so our search for frameworks provided by funding agencies was limited to the UK. It is possible that searches of funders operating in other geographical jurisdictions may have identified other studies. We are also aware that the way in which we have defined the process of dissemination and our judgements as to what constitutes sufficient detail may have resulted in some frameworks being excluded that others may have included or vice versa. Given this, and as an aid to transparency, we have included the list of excluded papers as Additional File 2 , Appendix 2 so as to allow readers to assess our, and make their own, judgements on the literature identified.

Despite these potential limitations, in this review we have identified 33 frameworks that are available and could be used to help guide dissemination planning and activity. By way of contrast, a recent systematic review of the knowledge transfer and exchange literature (with broader aims and scope) [ 61 ] identified five organising frameworks developed to guide knowledge transfer and exchange initiatives (defined as involving more than one way communications and involving genuine interaction between researchers and target audiences) [ 13 - 15 , 62 , 63 ]. All were identified by our searches, but only three met our specific inclusion criteria of providing sufficient dissemination process detail [ 13 - 15 ]. One reviewed methods for assessment of research utilisation in policy making [ 62 ], whilst the other reviewed knowledge mapping as a tool for understanding the many knowledge creation and translation resources and processes in a health system [ 63 ].

There is a large amount of theoretical convergence among the identified frameworks. This all the more striking given the wide range of theoretical approaches that could be applied in the context of research dissemination [ 64 ], and the relative lack of cross-referencing between the included frameworks. Three distinct but interlinked theories appear to underpin (at least in part) 28 of the included frameworks. There has been some criticism of health communications that are overly reliant on linear messenger-receiver models and do not draw upon other aspects of communication theory [ 65 ]. Although researcher focused, the included frameworks appear more participatory than simple messenger-receiver models, and there is recognition of the importance of context and emphasis on the key to successful dissemination being dependent on the need for interaction with the end user.

As we highlight in the introduction, there is recognition among international funders both of the importance of and their role in the dissemination of research [ 9 ]. Given the current political emphasis on reducing deficiencies in the uptake of knowledge about the effects of interventions into routine practice, funders could be making and advocating more systematic use of conceptual frameworks in the planning of research dissemination.

Rather than asking applicants to briefly indicate how findings arising from their proposed research will be disseminated (as seems to be the case in the UK), funding agencies could consider encouraging grant applicants to adopt a theoretically-informed approach to their research dissemination. Such an approach could be made a conditional part of any grant application process; an organising framework such as those described in this review could be used to demonstrate the rationale and understanding underpinning their proposed plans for dissemination. More systematic use of conceptual frameworks would then provide opportunities to evaluate across a range of study designs whether utilising any of the identified frameworks to guide research dissemination does in fact enhance the uptake of research findings in policy and practice.

There are currently a number of theoretically-informed frameworks available to researchers that could be used to help guide their dissemination planning and activity. Given the current emphasis on enhancing the uptake of knowledge about the effects of interventions into routine practice, funders could consider encouraging researchers to adopt a theoretically informed approach to their research dissemination.

Competing interests

Paul Wilson is an Associate Editor of Implementation Science. All decisions on this manuscript were made by another senior editor. Paul Wilson works for, and has contributed to the development of the CRD framework which is included in this review. The author(s) declare that they have no other competing interests.

Authors' contributions

All authors contributed to the conception, design, and analysis of the review. All authors were involved in the writing of the first and all subsequent versions of the paper. All authors read and approved the final manuscript. Paul Wilson is the guarantor.

Supplementary Material

Appendix 1: Database search strategies . This file includes details of the database specific search strategies used in the review.

Appendix 2: Full-text papers assessed for eligibility but excluded from the review . This file includes details of full-text papers assessed for eligibility but excluded from the review.

Acknowledgements

This review was undertaken as part of a wider project funded by the MRC Population Health Sciences Research Network (Ref: PHSRN 11). The views expressed in this paper are those of the authors alone.

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Penn State University Libraries

Empirical research in the social sciences and education.

• What is Empirical Research and How to Read It
• Finding Empirical Research in Library Databases
• Designing Empirical Research
• Ethics, Cultural Responsiveness, and Anti-Racism in Research
• Citing, Writing, and Presenting Your Work

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions to be answered
• Definition of the population, behavior, or phenomena being studied
• Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction: sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
• Results: sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion: sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Reading and Evaluating Scholarly Materials

Reading research can be a challenge. However, the tutorials and videos below can help. They explain what scholarly articles look like, how to read them, and how to evaluate them:

• CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
• IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
• Credo Video: How to Read Scholarly Materials (4 min.)
• Credo Tutorial: How to Read Scholarly Materials
• Credo Tutorial: Evaluating Information
• Credo Video: Evaluating Statistics (4 min.)
• Credo Tutorial: Evaluating for Diverse Points of View
• Next: Finding Empirical Research in Library Databases >>
• Last Updated: Aug 13, 2024 3:16 PM
• URL: https://guides.libraries.psu.edu/emp

Implications in research: A quick guide

Last updated

11 January 2024

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Implications are a bridge between data and action, giving insight into the effects of the research and what it means. It's a chance for researchers to explain the  why  behind the research. 

When writing a research paper , reviewers will want to see you clearly state the implications of your research. If it's missing, they’ll likely reject your article. 

Let's explore what research implications are, why they matter, and how to include them in your next article or research paper. 

• What are implications in research?

Research implications are the consequences of research findings. They go beyond results and explore your research’s ramifications. 

Researchers can connect their research to the real-world impact by identifying the implications. These can inform further research, shape policy, or spark new solutions to old problems. 

Always clearly state your implications so they’re obvious to the reader. Never leave the reader to guess why your research matters. While it might seem obvious to you, it may not be evident to someone who isn't a subject matter expert. 

For example, you may do important sociological research with political implications. If a policymaker can't understand or connect those implications logically with your research, it reduces your impact.

• What are the key features of implications?

When writing your implications, ensure they have these key features: 

Implications should be clear, concise, and easily understood by a broad audience. You'll want to avoid overly technical language or jargon. Clearly stating your implications increases their impact and accessibility. 

Implications should link to specific results within your research to ensure they’re grounded in reality. You want them to demonstrate an impact on a particular field or research topic . 

Evidence-based

Give your implications a solid foundation of evidence. They need to be rational and based on data from your research, not conjecture. An evidence-based approach to implications will lend credibility and validity to your work.

Implications should take a balanced approach, considering the research's potential positive and negative consequences. A balanced perspective acknowledges the challenges and limitations of research and their impact on stakeholders. 

Future-oriented

Don't confine your implications to their immediate outcomes. You can explore the long-term effects of the research, including the impact on future research, policy decisions, and societal changes. Looking beyond the immediate adds more relevance to your research. 

When your implications capture these key characteristics, your research becomes more meaningful, impactful, and engaging. 

• Types of implications in research

The implications of your research will largely depend on what you are researching. 

However, we can broadly categorize the implications of research into two types: 

Practical: These implications focus on real-world applications and could improve policies and practices.

Theoretical: These implications are broader and might suggest changes to existing theories of models of the world. 

You'll first consider your research's implications in these two broad categories. Will your key findings have a real-world impact? Or are they challenging existing theories? 

Once you've established whether the implications are theoretical or practical, you can break your implication into more specific types. This might include: 

Political implications: How findings influence governance, policies, or political decisions

Social implications: Effects on societal norms, behaviors, or cultural practices

Technological implications: Impact on technological advancements or innovation

Clinical implications: Effects on healthcare, treatments, or medical practices

Commercial or business-relevant implications: Possible strategic paths or actions

Implications for future research: Guidance for future research, such as new avenues of study or refining the study methods

When thinking about the implications of your research, keep them clear and relevant. Consider the limitations and context of your research. 

For example, if your study focuses on a specific population in South America, you may not be able to claim the research has the same impact on the global population. The implication may be that we need further research on other population groups. 

• Understanding recommendations vs. implications

While "recommendations" and "implications" may be interchangeable, they have distinct roles within research.

Recommendations suggest action. They are specific, actionable suggestions you could take based on the research. Recommendations may be a part of the larger implication. 

Implications explain consequences. They are broader statements about how the research impacts specific fields, industries, institutions, or societies. 

Within a paper, you should always identify your implications before making recommendations. 

While every good research paper will include implications of research, it's not always necessary to include recommendations. Some research could have an extraordinary impact without real-world recommendations. 

• How to write implications in research

Including implications of research in your article or journal submission is essential. You need to clearly state your implications to tell the reviewer or reader why your research matters. 

Because implications are so important, writing them can feel overwhelming.

Here’s our step-by-step guide to make the process more manageable:

1. Summarize your key findings

Start by summarizing your research and highlighting the key discoveries or emerging patterns. This summary will become the foundation of your implications. 

2. Identify the implications

Think critically about the potential impact of your key findings. Consider how your research could influence practices, policies, theories, or societal norms. 

Address the positive and negative implications, and acknowledge the limitations and challenges of your research. 

If you still need to figure out the implications of your research, reread your introduction. Your introduction should include why you’re researching the subject and who might be interested in the results. This can help you consider the implications of your final research. 

3. Consider the larger impact

Go beyond the immediate impact and explore the implications on stakeholders outside your research group. You might include policymakers, practitioners, or other researchers.

4. Support with evidence

Cite specific findings from your research that support the implications. Connect them to your original thesis statement. 

You may have included why this research matters in your introduction, but now you'll want to support that implication with evidence from your research. 

Your evidence may result in implications that differ from the expected impact you cited in the introduction of your paper or your thesis statement. 

5. Review for clarity

Review your implications to ensure they are clear, concise, and jargon-free. Double-check that your implications link directly to your research findings and original thesis statement. 

Following these steps communicates your research implications effectively, boosting its long-term impact. 

Where do implications go in your research paper?

Implications often appear in the discussion section of a research paper between the presentation of findings and the conclusion. 

Putting them here allows you to naturally transition from the key findings to why the research matters. You'll be able to convey the larger impact of your research and transition to a conclusion.

• Examples of research implications

Thinking about and writing research implications can be tricky. 

To spark your critical thinking skills and articulate implications for your research, here are a few hypothetical examples of research implications: 

Teaching strategies

A study investigating the effectiveness of a new teaching method might have practical implications for educators. 

The research might suggest modifying current teaching strategies or changing the curriculum’s design. 

There may be an implication for further research into effective teaching methods and their impact on student testing scores. 

Social media impact

A research paper examines the impact of social media on teen mental health. 

Researchers find that spending over an hour on social media daily has significantly worse mental health effects than 15 minutes. 

There could be theoretical implications around the relationship between technology and human behavior. There could also be practical implications in writing responsible social media usage guidelines. 

Disease prevalence

A study analyzes the prevalence of a particular disease in a specific population. 

The researchers find this disease occurs in higher numbers in mountain communities. This could have practical implications on policy for healthcare allocation and resource distribution. 

There may be an implication for further research into why the disease appears in higher numbers at higher altitudes.

These examples demonstrate the considerable range of implications that research can generate.

Clearly articulating the implications of research allows you to enhance the impact and visibility of your work as a researcher. It also enables you to contribute to societal advancements by sharing your knowledge.

The implications of your work could make positive changes in the world around us.

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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

• disquisition
• examination
• exploration
• inquisition
• investigation
• delve (into)
• inquire (into)
• investigate
• look (into)

Examples of research in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'research.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle French recerche , from recercher to go about seeking, from Old French recerchier , from re- + cerchier, sercher to search — more at search

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

• marketing research
• market research
• operations research
• oppo research

research and development

• research park
• translational research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 4 Sep. 2024.

Kids Definition

Kids definition of research.

Kids Definition of research  (Entry 2 of 2)

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Britannica English: Translation of research for Arabic Speakers

Britannica.com: Encyclopedia article about research

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Home > Blog >

Data analysis in qualitative research, theertha raj, august 30, 2024.

While numbers tell us "what" and "how much," qualitative data reveals the crucial "why" and "how." But let's face it - turning mountains of text, images, and observations into meaningful insights can be daunting.

This guide dives deep into the art and science of how to analyze qualitative data. We'll explore cutting-edge techniques, free qualitative data analysis software, and strategies to make your analysis more rigorous and insightful. Expect practical, actionable advice on qualitative data analysis methods, whether you're a seasoned researcher looking to refine your skills or a team leader aiming to extract more value from your qualitative data.

What is qualitative data?

Qualitative data is non-numerical information that describes qualities or characteristics. It includes text, images, audio, and video. 

This data type captures complex human experiences, behaviors, and opinions that numbers alone can't express.

A qualitative data example can include interview transcripts, open-ended survey responses, field notes from observations, social media posts and customer reviews

Importance of qualitative data

Qualitative data is vital for several reasons:

• It provides a deep, nuanced understanding of complex phenomena.
• It captures the 'why' behind behaviors and opinions.
• It allows for unexpected discoveries and new research directions.
• It puts people's experiences and perspectives at the forefront.
• It enhances quantitative findings with depth and detail.

What is data analysis in qualitative research?

Data analysis in qualitative research is the process of examining and interpreting non-numerical data to uncover patterns, themes, and insights. It aims to make sense of rich, detailed information gathered through methods like interviews, focus groups, or observations.

This analysis moves beyond simple description. It seeks to understand the underlying meanings, contexts, and relationships within the data. The goal is to create a coherent narrative that answers research questions and generates new knowledge.

How is qualitative data analysis different from quantitative data analysis?

Qualitative and quantitative data analyses differ in several key ways:

• Data type: Qualitative analysis uses non-numerical data (text, images), while quantitative analysis uses numerical data.
• Approach: Qualitative analysis is inductive and exploratory. Quantitative analysis is deductive and confirmatory.
• Sample size: Qualitative studies often use smaller samples. Quantitative studies typically need larger samples for statistical validity.
• Depth vs. breadth: Qualitative analysis provides in-depth insights about a few cases. Quantitative analysis offers broader insights across many cases.
• Subjectivity: Qualitative analysis involves more subjective interpretation. Quantitative analysis aims for objective, statistical measures.

What are the 3 main components of qualitative data analysis?

The three main components of qualitative data analysis are:

• Data reduction: Simplifying and focusing the raw data through coding and categorization.
• Data display: Organizing the reduced data into visual formats like matrices, charts, or networks.
• Conclusion drawing/verification: Interpreting the displayed data and verifying the conclusions.

These components aren't linear steps. Instead, they form an iterative process where researchers move back and forth between them throughout the analysis.

How do you write a qualitative analysis?

Step 1: organize your data.

Start with bringing all your qualitative research data in one place. A repository can be of immense help here. Transcribe interviews , compile field notes, and gather all relevant materials.

Immerse yourself in the data. Read through everything multiple times.

Step 2: Code & identify themes

Identify and label key concepts, themes, or patterns. Group related codes into broader themes or categories. Try to connect themes to tell a coherent story that answers your research questions.

Pick out direct quotes from your data to illustrate key points.

Step 3: Interpret and reflect

Explain what your results mean in the context of your research and existing literature.

Als discuss, identify and try to eliminate potential biases or limitations in your analysis. 

Summarize main insights and their implications.

What are the 5 qualitative data analysis methods?

Thematic Analysis Identifying, analyzing, and reporting patterns (themes) within data.

Content Analysis Systematically categorizing and counting the occurrence of specific elements in text.

Grounded Theory Developing theory from data through iterative coding and analysis.

Discourse Analysis Examining language use and meaning in social contexts.

Narrative Analysis Interpreting stories and personal accounts to understand experiences and meanings.

Each method suits different research goals and data types. Researchers often combine methods for comprehensive analysis.

What are the 4 data collection methods in qualitative research?

When it comes to collecting qualitative data, researchers primarily rely on four methods.

• Interviews : One-on-one conversations to gather in-depth information.
• Focus Groups : Group discussions to explore collective opinions and experiences.
• Observations : Watching and recording behaviors in natural settings.
• Document Analysis : Examining existing texts, images, or artifacts.

Researchers often use multiple methods to gain a comprehensive understanding of their topic.

How is qualitative data analysis measured?

Unlike quantitative data, qualitative data analysis isn't measured in traditional numerical terms. Instead, its quality is evaluated based on several criteria. 

Trustworthiness is key, encompassing the credibility, transferability, dependability, and confirmability of the findings. The rigor of the analysis - the thoroughness and care taken in data collection and analysis - is another crucial factor. 

Transparency in documenting the analysis process and decision-making is essential, as is reflexivity - acknowledging and examining the researcher's own biases and influences. 

Employing techniques like member checking and triangulation all contribute to the strength of qualitative analysis.

Benefits of qualitative data analysis

The benefits of qualitative data analysis are numerous. It uncovers rich, nuanced understanding of complex phenomena and allows for unexpected discoveries and new research directions. 

By capturing the 'why' behind behaviors and opinions, qualitative data analysis methods provide crucial context. 

Qualitative analysis can also lead to new theoretical frameworks or hypotheses and enhances quantitative findings with depth and detail. It's particularly adept at capturing cultural nuances that might be missed in quantitative studies.

Challenges of Qualitative Data Analysis

Researchers face several challenges when conducting qualitative data analysis. 

Managing and making sense of large volumes of rich, complex data can lead to data overload. Maintaining consistent coding across large datasets or between multiple coders can be difficult. 

There's a delicate balance to strike between providing enough context and maintaining focus on analysis. Recognizing and mitigating researcher biases in data interpretation is an ongoing challenge. 

The learning curve for qualitative data analysis software can be steep and time-consuming. Ethical considerations, particularly around protecting participant anonymity while presenting rich, detailed data, require careful navigation. Integrating different types of data from various sources can be complex. Time management is crucial, as researchers must balance the depth of analysis with project timelines and resources. Finally, communicating complex qualitative insights in clear, compelling ways can be challenging.

Best Software to Analyze Qualitative Data

G2 rating: 4.6/5

Pricing: Starts at $30 monthly.

Looppanel is an AI-powered research assistant and repository platform that can make it 5x faster to get to insights, by automating all the manual, tedious parts of your job. 

Here’s how Looppanel’s features can help with qualitative data analysis:

• Automatic Transcription: Quickly turn speech into accurate text; it works across 8 languages and even heavy accents, with over 90% accuracy.
• AI Note-Taking: The research assistant can join you on calls and take notes, as well as automatically sort your notes based on your interview questions.
• Automatic Tagging: Easily tag and organize your data with free AI tools.
• Insight Generation: Create shareable insights that fit right into your other tools.
• Repository Search: Run Google-like searches within your projects and calls to find a data snippet/quote in seconds
• Smart Summary: Ask the AI a question on your research, and it will give you an answer, using extracts from your data as citations.

Looppanel’s focus on automating research tasks makes it perfect for researchers who want to save time and work smarter.

G2 rating: 4.7/5

Pricing: Free version available, with the Plus version costing $20 monthly.

ChatGPT, developed by OpenAI, offers a range of capabilities for qualitative data analysis including:

• Document analysis : It can easily extract and analyze text from various file formats.
• Summarization : GPT can condense lengthy documents into concise summaries.
• Advanced Data Analysis (ADA) : For paid users, Chat-GPT offers quantitative analysis of data documents.
• Sentiment analysis: Although not Chat-GPT’s specialty, it can still perform basic sentiment analysis on text data.

ChatGPT's versatility makes it valuable for researchers who need quick insights from diverse text sources.

How to use ChatGPT for qualitative data analysis

ChatGPT can be a handy sidekick in your qualitative analysis, if you do the following:

• Use it to summarize long documents or transcripts
• Ask it to identify key themes in your data
• Use it for basic sentiment analysis
• Have it generate potential codes based on your research questions
• Use it to brainstorm interpretations of your findings

G2 rating: 4.7/5 Pricing: Custom

Atlas.ti is a powerful platform built for detailed qualitative and mixed-methods research, offering a lot of capabilities for running both quantitative and qualitative research.

It’s key data analysis features include:

• Multi-format Support: Analyze text, PDFs, images, audio, video, and geo data all within one platform.
• AI-Powered Coding: Uses AI to suggest codes and summarize documents.
• Collaboration Tools: Ideal for teams working on complex research projects.
• Data Visualization: Create network views and other visualizations to showcase relationships in your data.

G2 rating: 4.1/5 Pricing: Custom

NVivo is another powerful platform for qualitative and mixed-methods research. It’s analysis features include:

• Data Import and Organization: Easily manage different data types, including text, audio, and video.
• AI-Powered Coding: Speeds up the coding process with machine learning.
• Visualization Tools: Create charts, graphs, and diagrams to represent your findings.
• Collaboration Features: Suitable for team-based research projects.

NVivo combines AI capabilities with traditional qualitative analysis tools, making it versatile for various research needs.

Can Excel do qualitative data analysis?

Excel can be a handy tool for qualitative data analysis, especially if you're just starting out or working on a smaller project. While it's not specialized qualitative data analysis software, you can use it to organize your data, maybe putting different themes in different columns. It's good for basic coding, where you label bits of text with keywords. You can use its filter feature to focus on specific themes. Excel can also create simple charts to visualize your findings. But for bigger or more complex projects, you might want to look into software designed specifically for qualitative data analysis. These tools often have more advanced features that can save you time and help you dig deeper into your data.

How do you show qualitative analysis?

Showing qualitative data analysis is about telling the story of your data. In qualitative data analysis methods, we use quotes from interviews or documents to back up our points. Create charts or mind maps to show how different ideas connect, which is a common practice in data analysis in qualitative research. Group your findings into themes that make sense. Then, write it all up in a way that flows, explaining what you found and why it matters.

What is the best way to analyze qualitative data?

There's no one-size-fits-all approach to how to analyze qualitative data, but there are some tried-and-true steps. 

Start by getting your data in order. Then, read through it a few times to get familiar with it. As you go, start marking important bits with codes - this is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes - how do they connect? 

Finally, think about what it all means in the bigger picture of your research. Remember, it's okay to go back and forth between these steps as you dig deeper into your data. Qualitative data analysis software can be a big help in this process, especially for managing large amounts of data.

In qualitative methods of test analysis, what do test developers do to generate data?

Test developers in qualitative research might sit down with people for in-depth chats or run group discussions, which are key qualitative data analysis methods. They often use surveys with open-ended questions that let people express themselves freely. Sometimes, they'll observe people in their natural environment, taking notes on what they see. They might also dig into existing documents or artifacts that relate to their topic. The goal is to gather rich, detailed information that helps them understand the full picture, which is crucial in data analysis in qualitative research.

Which is not a purpose of reflexivity during qualitative data analysis?

Reflexivity in qualitative data analysis isn't about proving you're completely objective. That's not the goal. Instead, it's about being honest about who you are as a researcher. It's recognizing that your own experiences and views might influence how you see the data. By being upfront about this, you actually make your research more trustworthy. It's also a way to dig deeper into your data, seeing things you might have missed at first glance. This self-awareness is a crucial part of qualitative data analysis methods.

What is a qualitative data analysis example?

A simple example is analyzing customer feedback for a new product. You might collect feedback, read through responses, create codes like "ease of use" or "design," and group similar codes into themes. You'd then identify patterns and support findings with specific quotes. This process helps transform raw feedback into actionable insights.

How to analyze qualitative data from a survey?

First, gather all your responses in one place. Read through them to get a feel for what people are saying. Then, start labeling responses with codes - short descriptions of what each bit is about. This coding process is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes. Are certain ideas coming up a lot? Do different groups of people have different views? Use actual quotes from your survey to back up what you're seeing. Think about how your findings relate to your original research questions. 

Which one is better, NVivo or Atlas.ti?

NVivo is known for being user-friendly and great for team projects. Atlas.ti shines when it comes to visual mapping of concepts and handling geographic data. Both can handle a variety of data types and have powerful tools for qualitative data analysis. The best way to decide is to try out both if you can. 

While these are powerful tools, the core of qualitative data analysis still relies on your analytical skills and understanding of qualitative data analysis methods.

Do I need to use NVivo for qualitative data analysis?

You don't necessarily need NVivo for qualitative data analysis, but it can definitely make your life easier, especially for bigger projects. Think of it like using a power tool versus a hand tool - you can get the job done either way, but the power tool might save you time and effort. For smaller projects or if you're just starting out, you might be fine with simpler tools or even free qualitative data analysis software. But if you're dealing with lots of data, or if you need to collaborate with a team, or if you want to do more complex analysis, then specialized qualitative data analysis software like NVivo can be a big help. It's all about finding the right tool for your specific research needs and the qualitative data analysis methods you're using.

Here’s a guide that can help you decide.

How to use NVivo for qualitative data analysis

First, you import all your data - interviews, documents, videos, whatever you've got. Then you start creating "nodes," which are like folders for different themes or ideas in your data. As you read through your material, you highlight bits that relate to these themes and file them under the right nodes. NVivo lets you easily search through all this organized data, find connections between different themes, and even create visual maps of how everything relates.

How much does NVivo cost?

NVivo's pricing isn't one-size-fits-all. They offer different plans for individuals, teams, and large organizations, but they don't publish their prices openly. Contact the team here for a custom quote.

What are the four steps of qualitative data analysis?

While qualitative data analysis is often iterative, it generally follows these four main steps:

1. Data Collection: Gathering raw data through interviews, observations, or documents.

2. Data Preparation: Organizing and transcribing the collected data.

3. Data Coding: Identifying and labeling important concepts or themes in the data.

4. Interpretation: Drawing meaning from the coded data and developing insights.

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New research findings show Scottish independence and Brexit are different kinds of nationalism

• Publishing date: 3 September 2024

The latest chapter of the 41st British Social Attitudes report, published today by the National Centre for Social Research (NatCen), finds that people who support Scottish independence are more likely than opponents to have an inclusive, ‘civic’ understanding of what it means to be ‘truly Scottish’. In contrast, those who back Brexit are more likely than Remainers to have an exclusive, ‘ethnic’ conception of what it is to be British.

Those with a civic conception of national identity believe someone can become a member of a nation through upholding a certain set of shared values or respecting a country’s political institutions. Those with an ethnic conception believe membership of a nation is based on ancestry and/or place of birth, things that by definition a person cannot change later in life.

The argument for Brexit was more exclusive in tone, focusing on sovereignty and concern about immigration. In contrast, the campaign for Scottish independence has argued that Scotland needs to pool its sovereignty with the rest of the EU and that it should welcome migrants. 

These differences are reflected in the attitudes of those who support independence and those who back Brexit.

Scots take an inclusive approach to independence 

Only 50% of those who support Scottish independence say that being born in Scotland is important to being ‘truly Scottish’, compared with 59% of those who oppose independence. Meanwhile, 78% of Brexit supporters believe that being born in Britain matters to ‘being truly British’, whereas only 45% of Remainers feel that way.

While 42% of those who support independence say that having Scottish ancestry is important to being Scottish, this is rather less than the 51% that oppose independence who take that view. In contrast, 65% of those who back Brexit say that having British ancestry matters to being British, compared with just 28% of those who would vote to remain in the EU.

These differences are starkly reflected in attitudes towards immigration. Those in favour of being outside the EU are also more likely than those who wish to remain to endorse negative statements about immigrants. 57% of opponents of EU membership agree that immigrants increase crime rates, compared with just 17% of those who would vote to remain. In contrast, those who would vote yes to independence are less likely than those who would back no to agree with these negative statements. 32% of those who support Scotland remaining part of the UK agree that immigrants increase crime rates, compared with only 19% of those who would vote for Scottish independence.

In our accompanying Scottish Social Attitudes, we found 46% of people in Scotland feel that having Scottish ancestry is important to being Scottish, while British Social Attitudes found only 32% of people in England say that British ancestry is important to being British. 

Sir John Curtice, Senior Research Fellow at the National Centre for Social Research said: “Our systematic comparison of people’s understanding of national identity on the two sides of the border turns up a surprise. Although Scottish identity has been promoted, not least by the Scottish National Party (SNP), as an inclusive, ‘civic’ identity, in fact, having Scottish family background is thought by many to be as a key ingredient of being Scottish. This may reflect the fact that, in the absence of statehood and a legal definition of who is Scottish definition, people are more likely to think of Scottish identity as a social attribute rather than a political phenomenon."

Additional Information

Related content, is scottish nationalism different.

How Brexit reshaped party politics

More people now take an inclusive approach to who can call themselves British

British social attitudes 41 | national identity.

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Free Play Matters: Promoting Kindergarten Children’s Science Learning Using Questioning Strategies during Loose Parts Play

• Published: 02 September 2024

Cite this article

• Han Qi Zeng 1 &
• Siew Chin Ng   ORCID: orcid.org/0000-0003-1353-5971 1  

Early science inquiries and experiences increase young children’s awareness and interest for science. The importance of promoting science process skills which bolster children’s confidence to formulate and communicate personal ideas have been emphasised by international guidelines. As Loose Parts Play (LPP) is a form of free play involving open-ended play materials, its flexible nature promotes active exploration with materials that encourages children’s interaction with science-related experiences. This teacher action research aims to explore the influence of open-ended questions on children’s science process skills, as well as the scientific concepts that children are capable of exploring independently during play experiences. Analyses draw on video- and audio-recorded observation, child observation notes, and teacher journals. A total of 180 open-ended questions were employed by the teacher-researcher and 155 instances of science process skills were observed in a group of five-year-old children. Findings revealed that periods of uninterrupted play time followed by open-ended questions, extend children’s science process skills, and add complexity to their scientific exploration. Furthermore, children were observed to self-initiate exploration of scientific concepts, such as transforming materials and changing motion, during these uninterrupted play periods. Overall, this teacher action research highlights the pivotal role that educators play in young children’s playful learning experiences, where their timely use of open-ended questions has the capacity to facilitate children’s early science learning during LPP. This study serves to define an educator’s role within student-driven or child-initiated learning experiences, as well as guide educators in the utility of loose part materials, provision of uninterrupted play periods, and planning of open-ended questions to stimulate children’s science exploration.

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Acknowledgements

This paper is based on Han Qi Zeng’s teacher-inquiry project under the supervision of Siew Chin Ng. The authors wish to thank the Singapore University of Social Sciences Early Childhood Education faculty and the participating school for the field opportunity, as well as the children and teachers for participating in this research. The views expressed in this paper are the authors’ and do not necessarily represent the views of the institution.

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The right care in the right place: a scoping review of digital health education and training for rural healthcare workers

• Leanna Woods 1 , 2 ,
• Priya Martin 3 ,
• Johnson Khor 1 , 4 ,
• Lauren Guthrie 1 &
• Clair Sullivan 1 , 2 , 5  

BMC Health Services Research volume  24 , Article number:  1011 ( 2024 ) Cite this article

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Digital health offers unprecedented opportunities to enhance health service delivery across vast geographic regions. However, these benefits can only be realized with effective capabilities and clinical leadership of the rural healthcare workforce. Little is known about how rural healthcare workers acquire skills in digital health, how digital health education or training programs are evaluated and the barriers and enablers for high quality digital health education and training.

To conduct a scoping review to identify and synthesize existing evidence on digital health education and training of the rural healthcare workforce.

Inclusion criteria

Sources that reported digital health and education or training in the healthcare workforce in any healthcare setting outside metropolitan areas.

We searched for published and unpublished studies written in English in the last decade to August 2023. The databases searched were PubMed, Embase, Scopus, CINAHL and Education Resources Information Centre. We also searched the grey literature (Google, Google Scholar), conducted citation searching and stakeholder engagement. The JBI Scoping Review methodology and PRISMA guidelines for scoping reviews were used.

Five articles met the eligibility criteria. Two case studies, one feasibility study, one micro-credential and one fellowship were described. The mode of delivery was commonly modular online learning. Only one article described an evaluation, and findings showed the train-the-trainer model was technically and pedagogically feasible and well received. A limited number of barriers and enablers for high quality education or training of the rural healthcare workforce were reported across macro (legal, regulatory, economic), meso (local health service and community) and micro (day-to-day practice) levels.

Conclusions

Upskilling rural healthcare workers in digital health appears rare. Current best practice points to flexible, blended training programs that are suitably embedded with interdisciplinary and collaborative rural healthcare improvement initiatives. Future work to advance the field could define rural health informatician career pathways, address concurrent rural workforce issues, and conduct training implementation evaluations.

Review registration number

Open Science Framework: https://doi.org/10.17605/OSF.IO/N2RMX .

Peer Review reports

Introduction

Globally, healthcare workers (HCWs) face multiple pressures simultaneously: increasing demand for care, co-morbidities and condition complexity, budget pressures, and rapid digital disruption [ 1 ]. The digital disruption in healthcare promises an unprecedented circumstance to improve outcomes and strengthen health systems [ 2 ]. However, this opportunity depends on a capable healthcare workforce with adequate skills and knowledge in data and emerging technologies [ 3 ]. HCW capability in digital health and clinical informatics is increasingly acknowledged as an essential component to the delivery of high-quality patient care [ 4 ]. Universities do not yet routinely teach these curricula in clinical degrees, and the capability gap in the current workforce is often filled by brief, reactive, and on-the-job training [ 5 ]. Sustainability of healthcare includes developing a skilled healthcare workforce educated and competent in digital health [ 6 ].

The rural healthcare workforce is faced with the location-based issues of resource constraints, workforce shortages, high staff turnover rates, stress, burnout, and an ageing workforce [ 7 ]. The World Health Organization has acknowledged in a recent report (2021) the complex challenge of shortage of healthcare workers globally in rural areas [ 7 ]. This report has acknowledged that the workforce density is lower than national averages in most of these areas. In places where there isn’t a national shortage, maldistribution of the workforce has been noted [ 7 ]. Digitally enabled models of care are well placed to enhance health service delivery across vast and distributed geographic regions. However, rural health service organizations require uplift to align with their metropolitan counterparts in workforce digital readiness [ 8 ]. Building digital health capability in rural settings is critical because higher digital health capability is associated with better outcomes, including the ability to maintain an accurate patient health record, track patient experience data, track the patient journey, and mitigate clinical risks [ 9 ]. Rurality is contributing to widening digital health inequities [ 10 ] with significant efforts required to adequately manage the rural digital divide [ 11 , 12 ]. Building digital capabilities of healthcare providers in rural and remote settings through education, training and support is needed [ 13 ].

Existing evidence on the education and training the rural healthcare workforce is limited. Firstly, while health science faculties are progressively integrating digital health into the undergraduate curricula for the future workforce [ 14 , 15 , 16 ], it is unclear how the education of current HCW is approached [ 14 ]. Despite global exemplars such as fellowship training for physicians [ 17 ], certification for nurses [ 18 ], and advanced education for clinical and non-clinical professionals [ 19 ], limited evidence of successful workforce programs to build digital health skills exist [ 4 ]. None focus on the rural healthcare setting.

Secondly, in literature reporting digital health in rural settings, there is a notable scarcity on workforce training programs. Existing studies focus on efficacy of delivered healthcare [ 20 , 21 ], workforce perceptions of digital health tool implementation [ 22 , 23 ] or are limited to training of specific interventions (e.g., clinical telehealth [ 24 ]). This review sought to explore the literature where these two gaps coexist, the intersection of digital health education and training and the rural healthcare workforce, and synthesize the available evidence on digital health education and training for the rural healthcare workforce.

Review question

The research questions for this review were:

What are the existing practices and approaches to digital health education and training for rural HCWs?

How has digital health education and training been evaluated following implementation?

What are the barriers and enablers for high quality digital health education and training in the rural healthcare workforce?

Participants

The review considered studies and reports on any members of the workforce in healthcare settings outside of metropolitan areas. The healthcare workforce refers to ‘all individuals who deliver or assist in the delivery of health services or support the operation of health care facilities’ [ 3 ]. All clinical (e.g., medical doctors, nurses, allied health professionals, pharmacists, Indigenous HCWs, pre-registration/qualification students undertaking placements in health care facilities) and non-clinical workers (e.g., administration, executive and management, clinical support, and volunteers) were considered regardless of professional body or government registration status. Patients, healthcare consumers, and the public were excluded.

The core concepts of digital health and training were combined in this review. Digital health and clinical informatics are often used interchangeably, and both were considered in this review. While digital health refers to the use of digital technologies for health [ 25 ], clinical informatics refers to more specialized practice of analyzing, designing, implementing and evaluating information and communication systems [ 26 ]. Specific digital health systems (e.g., IT infrastructure, telehealth, electronic medical records) were included. Training relates to the education or training initiatives (e.g., programs, curriculum, course) that build an individuals’ digital health capability to confidently use technologies to respond to the needs of consumers now and into the future [ 1 ]. Both education and training activities were considered. Education often refers to theoretical learning (e.g., by an academic institution, qualification), and training often teaches practical skills (e.g., employer-provided professional development, ‘just-in-time’ training) [ 3 , 24 ]. This review did not consider HCW education delivered at a distance through technologies (e.g., telesupervision for clinical skills training).

This review considered studies and reports from rural healthcare settings defined as outside metropolitan cities, inclusive of regional, rural, remote, and very remote settings. When the term ‘rural’ is used in this review, it refers to all areas outside major metropolitan cities as described by authors of individual studies and reports. All healthcare facilities across primary, secondary, and tertiary care settings were included in any country.

Types of sources

All research studies, irrespective of the study design, were considered. Reviews, conference abstracts and non-research sources (e.g., policy documents, program or course curriculum) were considered. The grey literature was included to capture reactionary training developed by rural health services that were not published as peer-reviewed research studies.

This review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews [ 27 ] and reported as per the Preferred Reporting of Systematic Reviews and Meta-analyses for scoping reviews (PRISMA-ScR) [ 28 ] (Additional file 1 ). The review protocol was registered in Open Science Framework [ https://doi.org/10.17605/OSF.IO/N2RMX ].

A scoping review approach was chosen over a systematic review to address a general, formative review question on this topic that is emerging in the literature and where the literature is complex and heterogenous [ 29 ]. An initial preliminary search of the topic in the academic databases, Cochrane Library, Open Science Framework and Prospero registry resulted in a very small number of relevant articles. It was determined that a broader search strategy and inclusion of non-research sources was required, consistent with the scoping review methodology [ 29 ]. Scoping review format is also well suited to the vast, diverse healthcare education topic across different disciplines, interventions and outcomes realised [ 30 ]. Mapping and synthesis across sources in this scoping review aims to inform research agendas and identify implications for policy and practice [ 31 ].

Deviations from the protocol

There were no deviations to the protocol.

Search strategy

The three phase JBI search process was followed. An initial limited search of PubMed was performed to identify keywords on the topic, followed by an analysis of the text words and index terms contained in the title and abstract. A subsequent preliminary search in Prospero registry, Cochrane Library and Open Science Framework informed the development of a full search strategy in PubMed. The search strategy, including all identified keyworks and index terms, was adapted for each included database and information source after refining the strategy with an information specialist. The reference lists of all included sources of evidence were screened for additional studies.

The review included only studies and reports in English (due to translation resourcing limitations) in the last 10 years (due to the relative novelty of the digital transformation of healthcare). The search was conducted in August 2023. The databases searched included PubMed, Scopus, Cumulative Index for Nursing and Allied Health Literature (CINAHL), Embase, and Education Resources Information Center (ERIC). Scopus was chosen over Web of Science as it provides 20% more coverage and the relative recency of articles indexed (publish date after 1995 [ 32 ]) was not a concern for our research question. The search for unpublished studies and grey literature included Google and Google Scholar, using a modified search strategy as required. In addition, national and international stakeholders ( n  = 29) from Asia, the Pacific Islands, Australia, USA and the UK known to have subject matter expertise on the topic were contacted via direct email. Stakeholders were asked to share any relevant work underway or otherwise undiscoverable using our scoping review methods. The full search strategy for each information source is provided in Additional file 2 .

Study selection

Following the search, identified articles were collated and uploaded into Covidence review software (Veritas Health Innovation Ltd; Melbourne, Australia) and duplicates removed. Two reviewers (among LW, JK and LG) then independently screened the title and abstract of each citation and selected studies that met the inclusion criteria. The full text articles were retrieved and uploaded into Covidence. These studies and reports were assessed independently by two reviewers (listed previously) for full assessment against the inclusion criteria. Any disagreements that arose between the reviewers at each stage of the selection process were resolved through discussion or with an additional reviewer (among LG and PM). Three meetings occurred to discuss any voting conflicts that occurred during title and abstract screening and full-text screening. Articles that did not satisfy the criteria were excluded with reasons for exclusion recorded. Search results and study selection process is presented in accordance to the PRISMA-ScR flow diagram (Fig. 1 ) [ 28 ]. Quality appraisal of selected studies was not conducted, consistent with scoping reviews methods [ 33 ].

Search results and source selection and inclusion process

Data extraction

Extracted data included the specific details about the participants, concept, context, study methods and key findings relevant to each review question. Data was extracted by one reviewer (JK) and checked by a second reviewer (LW). Data were extracted using the data extraction tool developed and piloted by the team (Additional file 3 ).

Data synthesis and presentation

The characteristics of the included studies were analyzed and organized in tabular format, accompanied by a narrative summary. Results of each research question was presented under separate headings. The data analysis for research question three (barriers and enablers of high-quality digital health education and training) was enhanced. We adopted the socio-institutional framework described by Smith et al [ 34 ] and used in education research [ 35 ] to classify macro, meso, micro level enablers and barriers to help improve the generalizability of the synthesized insights and identify stakeholders that are able to influence change. Gaps and limitations of the current literature were discovered from the evidence with recommendations for policy, practice and future research provided.

Study inclusion

Database searching yielded 1005 articles and stakeholder engagement yielded two articles. After removing duplicates, 660 articles were screened for title and abstract, after which 29 articles underwent full text review. Of the 29 articles, 24 articles were excluded: the setting was metropolitan or otherwise inadequately described as non-metropolitan ( n  = 6); the intervention was not a training or education initiative for digital health or clinical informatics ( n  = 16), or the population was not rural healthcare workers ( n  = 2). In total, following full-text screening, five articles were included in the final review (Fig.  1 ).

Characteristics of included studies

Of the five included articles, three were academic publications including two case studies [ 36 , 37 ] and one feasibility study [ 38 ] (Table 1 ). The two articles identified through stakeholder engagement presented course summaries [ 39 , 40 ] where one described a micro-credential [ 40 ] and the other described a fellowship [ 39 ]. Most articles ( n  = 3) were published recently between 2021 and 2023 [ 38 , 39 , 40 ]. Healthcare workforce settings were distributed across the continents of the United States of America [ 36 ], Asia [ 37 ], Africa [ 38 ] and Australia [ 39 , 40 ], with no articles reporting a setting in the European continent. Further study characteristics are available in Table 1 .

Review findings

What are the existing approaches to digital health education and training for rural hcws.

Training and education programs were needed due to identified gaps in knowledge, skills and expertise to support healthcare delivery in rural contexts with digital health [ 36 , 37 , 38 ], [ 40 ]. One article reported the target learners as village doctors, who may have “limited training and inadequate medical knowledge, yet they are generally the mainstay of health services” [ 37 ]. The mode of teaching in the included studies were four modular online learning courses [ 36 , 37 , 38 ], [ 40 ] and one fellowship [ 39 ]. Of the four modular online learning courses, one was supplemented by a facilitator-led train-the-trainer model [ 38 ], informed by an academic framework [ 41 ], with cohort-based discussion via a social media platform. The second was a certification in the form of a self-paced micro-credential completed individually [ 40 ]. Of the four modular online learning courses, the number of modules ranged from three to eight and covered a variety of digital health topics including innovation, commercialization, bioinformatics, technology use, data and information, professionalism, implementation and evaluation. One had a particular focus on information and communication technology tool use [ 37 ] while another focused on remote consulting [ 38 ]. The mode of delivery of the fellowship was not reported in the article.

Four [ 36 , 37 , 39 , 40 ] of the five included articles did not report an evaluation. One article in rural Tanzania described the evaluation of the train-the-trainer digital health training program using a mixed-method design [ 38 ]: (1) questionnaire informed by Kirkpatrick’s model of evaluation to capture knowledge gained and perceived behavior change on a Likert scale, (2) qualitative interviews to explore training experiences and views of remote consulting, and (3) document analysis from texts, emails and training reports [ 38 ]. Of the tier 1 trainees (senior medical figure trainers who were trained to educate their peers) that completed the questionnaire ( n  = 10, 83%), nine (90%) recommended the training program and reported receiving relevant skills and applying learning to daily work, demonstrating satisfaction, learning and perceived behavior change [ 38 ]. Overall, the feasibility study confirmed that remotely delivered training supported by cascade training was technically and pedagogically feasible and well received in rural Tanzania [ 38 ].

What are the barriers and enablers for high quality digital health education and training of the rural healthcare workforce?

Reported enablers and barriers are presented using the macro, meso, micro framework [ 34 ] (Table 2 ).

This scoping review reflects the scarcity of reported digital health education and training programs in existence for rural HCWs globally. This review responds to the World Health Organization (WHO) recommendation to design and enable access to continuing education and professional development programs that meet the needs of rural HCWs [ 7 ], and the Sustainable Development Goal for inclusive and equitable quality education [ 42 ].

Concurrent challenges of people (workforce), setting (rural) and content (digital health) are reported in included articles alongside enablers and barriers to education and training programs. Included studies reported a shortage of doctors and specialists [ 36 ], lack of technical knowledge [ 36 ] (people); higher cost of delivering rural healthcare, high burden of illness [ 40 ], medically underserved population due to rural hospital closures [ 36 ] (setting); and limited use of digital health tools due to coordination challenges among non-government organisations [ 37 ] (content). These additional macro, meso and micro level factors are described by authors firstly as influencing the need for digital health programs in rural settings, and secondly, as contributing to the challenges of implementing effective programs. The rural health workforce challenges in digital health education and training reflect the broader workforce development issues experienced globally [ 7 ]. While this review sought to identify workforce development programs, the WHO model indicates the need for attractiveness, recruitment and retention to enable workforce performance (i.e., appropriate and competent multidisciplinary teams providing care) and health system performance (i.e., improving universal health coverage) [ 7 ].

In low-resource settings such as rural areas, education and training may not be prioritized among other competing workload demands. As the value of digital health transformations are realized for strengthening healthcare systems [ 25 , 43 ], the value of digital health education or training programs may become realized. This value was evidenced in the implementation of the teleconsulting training intervention in rural Tanzania [ 38 ] in rapid response to supporting care delivery during the COVID-19 pandemic period. With evaluations of programs largely absent from an already small number of programs globally, it will be important for future research to focus on implementation evaluation studies. As Table 2 presents only limited enablers and barriers, more evidence is needed to build on the findings from this scoping review to inform strategies for policy and practice.

The interdisciplinarity of digital health presents challenges and opportunities for nurturing digital health expertise across the rural healthcare workforce. Included articles largely described the target learners of education and training programs as clinicians, practitioners and healthcare workforce. Walden et al. further indicated that users of online content may extend beyond rural health clinicians to healthcare administrators, researchers and providers relevant to address the regulatory factors of clinical validation and implementation [ 36 ]. Therefore, for their program of work, the University of Arkansas for Medical Sciences identified and fostered collaboration with an interprofessional team of clinicians, researchers, informaticists, a bioethicist, lawyers, technology investment experts, and educators [ 36 ]. No articles in the review described education or training health informaticians or similar digital health leadership role types, yet building defined career pathways for health informaticians is recommended [ 4 ]. Existing pedagogy shows that the learning principles of interprofessional practice is grounded in understanding one’s own practice as well as the practice of other health professionals and remains aligned to the educational needs of specific professions [ 44 ] (i.e., medicine, nursing, pharmacy). Defining new career pathways for interdisciplinary leaders in digital health within a specific clinical context, like the ‘rural health informatician’, will be important to identify or define the (hidden) specialized workforce.

Local, informal organizational initiatives for digital health learning were discovered alongside formal education or training programs in included studies. Programs were often reported in articles alongside concurrent digital health implementation or healthcare improvement programs, sometimes referred to as ‘outreach’ [ 36 ] activities. These informal initiatives included special interest groups, in-person conferences, networking events, working groups [ 36 ] and seminars [ 37 ]. Current evidence from this scoping review suggests that the efficacy and sustainability of education or training programs are reliant on integrated approaches, like the train-the-trainer [ 38 ] or academic organization approach [ 36 ], that foster translational research for rural healthcare improvement. As illustrated by Walden et al., success in digital health is likely to require a foundational environment where technologies can be discussed, developed and deployed [ 36 ]. Success in rural digital health skills acquisition likely requires a similar, longitudinal and collaborative approach beyond the confines of an online course completed individually. Previous research shows us that blended learning, which merges face-to-face with online learning, translates to better knowledge outcomes [ 44 ]. Blended learning can also overcome the barrier of rural HCWs travelling large distances to attend face-to-face training that comes at a great cost to themselves and the work unit. A key recommendation to improve the digital health training program described by Downie et al. was more face-to-face time with trainers, from the perspective of both trainee and facilitator [ 38 ]. This, however, can only be realized with targeted planning and budgeting of such offerings by involved rural healthcare organizations.

The opportunities to advance digital health education and training for rural HCWs are presented across the macro, meso and micro levels in the socio-institutional framework, with suggested relevant stakeholders suited to actioning the recommendations (Table  3 ). While the context for this is likely to vary across the globe, these recommendations and stakeholders are expected to provide a starting point to initiate a dialogue that can influence change. These recommendations are not meant to be prescriptive or rigid, but rather meant to flag actionable solutions that can be contextualized for any given setting.

Strengths and limitations

It is possible that there is a greater number of published educational and training programs than those reported in this review (i.e., publication bias). To mitigate this, we used a scoping review methodology and stakeholder engagement activity to identify unpublished or emerging programs that answer the review question but may not be discoverable in the academic databases. The review is limited to articles available in the English language. The small number of programs, heterogeneity of programs and limited evaluation of programs significantly limit generalizability of findings. Due to data availability, the barriers and enablers findings summary contain an overrepresentation from a small number of studies limiting conclusions that can be drawn.

Digital health offers the best opportunity for innovative sustainable change to address critical issues in health and care in rural settings. Workforce education and training initiatives in rural healthcare settings are scarce, largely delivered via online training, and are rarely evaluated. Current best practice points to flexible, blended (online and face-to-face) training programs that are suitably embedded with interdisciplinary, collaborative rural healthcare improvement initiatives. More research will expand the evidence base to deliver high-quality digital health education to strengthen rural healthcare delivery. Future work to advance the field could define rural health informatician career pathways, address concurrent rural workforce issues, and conduct implementation evaluations.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Cumulative Index for Nursing and Allied Health Literature

Education Resources Information Centre

Healthcare worker

Joanna Briggs Institute

Preferred Reporting of Systematic Reviews and Meta-analyses for scoping reviews

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LW, PM and CS designed the study. LW, PM, JK and LG acquired data; analyzed and interpreted results and drafted the manuscript and all subsequent drafts. CS read and contributed to manuscript drafts. All authors read and approved the final manuscript draft.

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Additional File 1. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.

Additional File 2. Full search strategy for each information source.

Additional file 3. data extraction instrument template., rights and permissions.

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Woods, L., Martin, P., Khor, J. et al. The right care in the right place: a scoping review of digital health education and training for rural healthcare workers. BMC Health Serv Res 24 , 1011 (2024). https://doi.org/10.1186/s12913-024-11313-4

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• Published: 03 September 2024

Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer

• Serena Di Cosimo 1 ,
• Marco Silvestri 1 , 2 ,
• Cinzia De Marco 1 ,
• Alessia Calzoni 2 , 3 ,
• Maria Carmen De Santis 4 , 5 ,
• Maria Grazia Carnevale 4 , 5 ,
• Carolina Reduzzi 6 ,
• Massimo Cristofanilli 6 &
• Vera Cappelletti 1  

Scientific Reports volume  14 , Article number:  20479 ( 2024 ) Cite this article

Metrics details

• Breast cancer
• Cancer genomics
• Tumour biomarkers

Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their standardized definition across platforms. Herein, we report the feasibility of using low-pass Whole Genome Sequencing to assess LSTs, copy number alterations (CNAs) and their relationship in individual circulating tumor cells (CTCs) of triple-negative breast cancer (TNBC) patients. Initial assessment of LSTs in breast cancer cell lines consistently showed wide-ranging values (median 22, range 4–33, mean 21), indicating heterogeneous CIN. Subsequent analysis of CTCs revealed LST values (median 3, range 0–18, mean 5), particularly low during treatment, suggesting temporal changes in CIN levels. CNAs averaged 30 (range 5–49), with loss being predominant. As expected, CTCs with higher LSTs values exhibited increased CNAs. A CNA-based classifier of individual patient-derived CTCs, developed using machine learning, identified genes associated with both DNA proliferation and repair, such as RB1 , MYC , and EXO1 , as significant predictors of CIN. The model demonstrated a high predictive accuracy with an Area Under the Curve (AUC) of 0.89. Overall, these findings suggest that sequencing CTCs holds the potential to facilitate CIN evaluation and provide insights into its dynamic nature over time, with potential implications for monitoring TNBC progression through iterative assessments.

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Introduction.

Breast cancer is a global health issue with approximately two and a half million new cases diagnosed annually worldwide 1 . Despite advances in screening, detection, and treatment, breast cancer remains the leading cause of cancer-related deaths among women 1 . The triple-negative (TNBC) subtype has the worst prognosis, emphasizing the need for improved care for both localized and metastatic patients 2 .

Chromosomal Instability (CIN) refers to the increased acquisition or loss of whole or fragmented chromosomes, and represents the most common form of genome instability in breast cancer 3 . Thus, improving our ability to assess CIN could offer promising insights into tumor progression and optimize patient care. Standard methods for evaluating CIN, such as DNA image cytometry and fluorescence in situ hybridization (FISH), are seldom used in the clinics due to their labor-intensive procedures and lack of high-throughput capabilities 4 . Alternative approaches including CIN70 5 and HET70 6 signatures, based on the expression of genes associated with aneuploidy and karyotype heterogeneity, or comparative genomic hybridization 7 have also been utilized, showing that increased CIN is associated with metastatic potential and dismal prognosis 5 , 6 , 7 . However, bulk analytical methods give a broad view of CIN without distinguishing between ongoing or past events that may not have continued. In addition, DNA image cytometry, FISH, and transcriptomic analysis face challenges in capturing the inherent cell-to-cell heterogeneity of CIN as they rely on pooled DNA samples 4 .

Single-cell sequencing (scDNAseq) is emerging as a promising approach to tackle the above listed challenges by providing accurate and quantitative CIN measures that are amenable to clinical use 8 . scDNAseq can provide insights into the underlying aberrant molecular pathways driving CIN, with DNA repair genes being prominent candidates 8 . Additionally, scDNAseq overcomes limitations and confounding factors associated with the use of bulk tissue, such as surrounding stromal tissue, tumor heterogeneity, and limited sample availability 8 . Importantly, scDNAseq can be applied to circulating tumor cells (CTCs), which are emerging as a significant resource for timely breast cancer molecular characterization 9 . Unlike invasive tumor tissue biopsy that is prone to sampling error, CTCs allow dynamic and repeatable assessment, representing the ideal source for longitudinal measuring of an evolving feature such as CIN 10 .

In this study, we leveraged our expertise in CTC genotyping by next-generation sequencing 11 to analyze CIN and underlying molecular alterations in TNBC patients. Specifically, we challenged low-pass Whole Genome Sequencing (lp-WGS) to determine the number of Large-Scale Transitions (LSTs) defined as contiguous regions of chromosomal breakage spanning at least 10 Mb 12 . The LST metric was chosen for its frequent use as a biomarker of CIN 8 , 13 . First, we tested the consistency of LST measurements using lp-WGS in a panel of breast cancer cell lines. Next, we extended our analyses to individual patient-derived CTCs collected at different clinical time-points, i.e., baseline, treatment, follow-up, and relapse. Finally, we developed a streamlined model for assessing CIN based on CTC copy number alterations (CNAs) within a specific set of genes.

As part of technical feasibility, we initially evaluated LSTs as a means of CIN evaluation in breast cancer cell lines undergoing whole genome amplification and lp-WGS at the single-cell level. The analyses were conducted on MDA-MB-453, MDA-MB-361, BT474, BT549, and ZR-75 cell lines in replicates as reported in Table 1 . We observed a wide range of LSTs (median 22, range 4–33), reflecting the heterogeneous nature of CIN both within individual cells and across different cell lines (Fig.  1 a).

Large-scale transitions in breast cancer cell lines and patient-derived individual CTCs. Distribution of large- scale transitions (LSTs)—defined as chromosomal breakpoints between adjacent regions spanning at least 10 megabases—in breast cancer cell lines ( a ) and patient-derived individual CTCs ( b ).

Notably, LSTs values were significantly and reproducibly determined for the tested cell lines (Table 1 ).

We next analyzed clinical samples from 12 patients with histologically confirmed TNBC, successfully profiling (> 400,000 reads) a total of 35 CTCs collected at various time points throughout the disease trajectory (Table 2 ).

LSTs in CTCs showed heterogeneity (median 3, range 0–18), with values lower than those observed in cell lines, especially during treatment (median 2, range 0–13). Median LSTs in CTCs from patients with and without metastases were 2 and 3.5, respectively; 3 in germ-line BRCA mutation carriers . The distribution of LSTs values displayed a bimodal shape (Fig.  1 b). However, its limited extent prevented definition of a clear threshold, prompting the use of the median number of LSTs to classify CTCs as either LST-low (number of LSTs < 3) or high (number of LSTs ≥ 3).

We next analyzed the CTC CNA profile. The mean number of CNAs per CTC was 30 (range 5–49), with deletions outnumbering amplifications at 401:291 (Supplementary Fig. 3). The most frequently lost or gained chromosomal regions and the corresponding genes are reported in Fig.  2 .

Copy number alterations in individual CTCs of TNBC patients. The heatmap shows CTCs in the columns according to their number of LSTs and classified as high when ≥ 3 (dark blue) or low when < 3 (yellow). The rows show the top-fifty altered genes by chromosomal arm, with red indicating gain and blue indicating loss.

Recurrent alterations involved 9p and 9q, containing ABL1 , NOTCH1 , and CDKN2A ; 10, containing MAPK8 and GATA3 ; and 22q, containing BCR , as expected and consistently with literature on genes involved in TNBC oncogenesis 14 . We also analyzed CNA with respect to LSTs. Compared to CTCs classified as LST-low, those with higher values had a numerical increase in CNAs overall, median CNAs in CTCs with high and low LSTs 22 and 13, p = 0.08, and a prevalence of copy number losses, particularly in homologous recombination deficiency (HDR) related genes, with 59% (13/22) of CTCs classified as LST-high and 31% (4/13) of the LST-low showing RAD51 , BLM , or WNR copy loss, p = 0.05. Oncogenic signaling pathways analysis showed that CTCs classified as LST-high were enriched for CNAs—either gains or losses—affecting NRF2, TP53, and TGF-beta signaling (Supplementary Fig. 1).

However, the question remained as to which factors most strongly influence LSTs. Therefore, we used a Random Forrest (RF) non parametric machine learning method to develop a CNA-based classifier of patient-derived CTCs with and without LSTs (Supplementary Fig. 2).

A total of 39 covariates were included in the model, consisting of CNAs of established HDR related 15 and TNBC driver 16 genes (Supplementary Table 1). RB1 , MYC , and EXO1 emerged as the most relevant predictors of CIN among all covariates, with variable importance index (VIMP) indicating that the prediction error rate would increase by up to 30% if the CNAs of these genes were randomly permuted in the model (Fig.  3 a).

Model performance evaluation. ( a ) Internal measure of variable importance (VIMP) of altered genes in CTCs harboring CIN. The VIMP shows decreases in classification accuracy when the values of a given variable are randomly permuted, while all other predictors remain unchanged in the model. The larger the VIMP of a variable, the more predictive the variable ( b ) Receiver operating characteristic curve (ROC) for prediction of LSTs based on the CNAs of breast cancer related genes profiled by lp-WGS and computed through a RF learning model. AUC (Area under the curve).

Strikingly, the RF model yielded an AUC of 0.89 indicating that the analysis of CNAs in a few genes might be sufficient to achieve reliable classification of CIN (Fig.  3 b).

Chromosomal instability is increasingly recognized as a cancer hallmark, crucial in initiation, progression, and metastasis, with implications for optimizing care 3 , 17 . However, its regular assessment is hindered by its dynamic nature and limitations in currently available tools 4 . Hence, there is a critical need to develop CIN biomarkers that are easily and reliably assessable to inform and guide clinical management, including in breast cancer patients. To the best of our knowledge, several studies have assessed the CNA of CTCs, but none have tackled CIN analysis 18 , 19 , 20 . In this study, we analyzed lp-WGS data to evaluate LSTs and CNAs in individual CTCs from women with TNBC, and to build a predictive classifier of CIN at the single-cell level achieving an AUC of 0.89. While our study is preliminary, we are the first to report a cost-effective sequencing assay such as lp-WGS for assessing LSTs in CTCs, the utilization of distinctive genetic features to evaluate complex phenomena, and ultimately, the development of a performing predictive model based on CNAs interactions. Additionally, we incorporated the assessment of CIN, a dynamic variable on CTCs, whose analysis can be repeated over time through a minimally invasive blood draw. These findings not only pave the way to a novel analytical approach for assessing CIN but also provide significant contributions to the field.

The distributions of LSTs values, both in breast cancer cell lines and individual CTCs, confirm the significant heterogeneity of CIN. This observation is consistent with existing literature, which suggests that the CIN underlying mechanisms leading to dysfunctional chromosome duplication and segregation can vary 21 . Interestingly, the LSTs values observed in CTCs, particularly those from recurrent patients, were not as elevated as expected. These findings align with prior research indicating low karyotypic variance during disease progression across various cancer types including the breast 22 . To reconcile this observation with the well-documented prevalence of CIN in cancer, the theory of the CIN paradox posits that tumors typically exhibit intermediate levels of CIN as excessively high levels are detrimental, while insufficient levels do not guarantee an advantage in terms of proliferation and survival 23 . In addition, the low LST values observed in recurrent breast cancer patients may be influenced by the number of CTCs analyzed potentially affecting the prevalence of CIN. This raises the question of deriving individuals' features from their single-cell data. To the best of our knowledge, few previous work estimated the required sample size, i.e., the number of cells to profile, to infer CIN from scDNAseq data 24 . Regarding CTCs, while some have suggested diagnosing cancer with CIN based on the presence of only one 25 to at least 3 unstable CTCs 26 , it is uncertain if this also applies to breast cancer. Therefore, further research is needed.

Several studies have characterized CNAs in TNBC tissue using high-resolution genomic data 16 . Consistent with these findings, CTC CNAs more frequently showed deletions than amplifications. Despite potential limitations of lp-WGS compared to higher resolution next-generation sequencing, we report that CTC chromosomal gains and losses occurred in regions where breast cancer-related genes are generally found, supporting that our findings were unlikely to be due to random sequencing dropout or due to amplification bias. For instance, CDKN2A and NOTCH1 were identified in loss regions 14 , 16 . It is also not surprising that CTCs with high LSTs were more frequently characterized by the loss of HDR related genes. However, whether this is the cause of LSTs or if, conversely, the loss of these genes is the consequence, we cannot ascertain. The fact remains that DNA repair genes alone do not fully explain CTC CIN. As already reported for tumor tissue, other factors such as mitotic errors, replication stress, telomere crisis, and breakage fusion bridge cycles 21 , among others, may also be at play. Therefore, we hypothesized that the simultaneous analysis of copy number changes in a set of selected genes could help define CTCs with and without LSTs. To this end, we utilized, for the first time in this context, the RF learning model which allowed us to examine the impact of different potential predictors in creating a predictive model 27 . Our findings indicate that RB1 , EXO1 , and MYC are the most significant predictors among all covariates for identifying LSTs, with a variable importance index exceeding 30%. These results align with preclinical evidence suggesting that the loss of G1/S control resulting from RB1 pathway inactivation, coupled with MYC -induced mitogen addition and DNA damage, leads to chromatid breaks and chromatid cohesion defects in mitotic cells 28 . These aberrations ultimately contribute to aneuploidy in the offspring cell population. Furthermore, LSTs represent a subset of chromosomal rearrangements, particularly evident when double-strand breaks are repaired through non-homologous end joining, as observed in BRCA-deficient environments 12 . Aligned with this, alterations of BRCA1 and BRCA2 demonstrated substantial predictive value within the developed classifier.

This study and its methods have several strengths, as the classifier presented here represents a resource for a deeper understanding of the origins and diversity of CIN. Our results focus attention on a narrow group of genes involved in fundamental cellular processes for maintaining genomic integrity. Additionally, our results support the broader application of CIN measures in clinical diagnostics, as sequencing techniques, which have been rarely used due to technical difficulties, are becoming more widespread and affordable every day. Finally, this work focuses on targets that may lead to potentially applicable therapies, beyond those traditionally suggested based on platinum 21 and taxane 29 for the most unstable tumors.

Despite these strengths, this study and the methods used also have weakness that should be noted. First, the number of LSTs is only one functional measure of CIN, and other measures exist, including telomere allele imbalance and loss of heterozygosis. Second, data on the single-cell nature of copy number or LST burden in single tumor cells in a large cohort are lacking, and technical limitations require that the data generated to date be interpreted with caution. Finally, RF cannot produce hypothesis testing results, such as relative risks, odds ratios, or p-values, as in classical regression methods, and its use is for model exploration. Hence, the data presented herein merit confirmation.

In conclusion, our study demonstrates the feasibility of low-resolution lp-WGS for assessing both LSTs and CNAs in TNBC CTCs at a single-cell level. As a proof-of-concept study, we developed a classifier of LSTs based on CNAs of genes involved both in HDR and replication process. Future research with larger sample sizes will be necessary to evaluate the clinical application of this assay, which lays the groundwork for leveraging CIN in precision oncology efforts.

Materials and methods

Sample processing.

For spiking experiments, five cell lines broadly representative of breast cancer, expressing (+) or lacking (−) the estrogen receptor (ER), and showing Human Epidermal Growth Factor Receptor 2 amplified (HER2+) or normal (HER2−) status were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). ZR75-1 (ER+/HER2−), MDA-MB-453 (ER−/HER2+), MDA-MB-361 (ER+/HER2+), and BT-549 (ER−/HER2−) were cultured in DMEM/F-12 (Lonza, Swizerland) medium supplemented with 10% fetal bovine serum, BT474 (ER+/HER2+) in Dulbecco’s Modified Eagle’s Medium (DMEM) (Sigma, Darmstadt, Germany). All culture media were supplemented with antibiotic–antimycotic Solution (100 ×) (Sigma, Darmstadt, Germany), 10% fetal bovine serum (FBS) (Sigma, Darmstadt, Germany) and L-glutamine (2 mM) (Invitrogen GmbH, USA), and tested negative for mycoplasma contamination. Single cells were manually captured under an inverted microscope using a p10 micropipette and directly spiked into healthy donor blood. Spiked-in samples were processed following the same protocols used for clinical samples.

Peripheral blood was collected from study patients in K2EDTA tubes (10 ml) and processed within 1 h of draw using the Parsortix platform (Angle plc, Guildford, UK) for size-based enrichment. Following enrichment, cells were harvested according to manufacturer’s instructions and fixed with 2% paraformaldehyde for 20 min at room temperature.

Cell isolation, amplification and sequencing

Enriched patient samples were processed using the DEPArray system (Menarini Silicon Biosystems, Bologna, IT) 11 . Individual cells were sorted based on morphological characteristics, DNA content, and fluorescence labeling against epithelial (CK, EpCAM, EGFR) and leukocyte (CD45, CD14, CD16) markers, as previously reported 11 . Subsequently, white blood cells expressing only leukocyte markers and single CTCs expressing either only epithelial markers or lacking any marker were recovered for downstream molecular analyses. WGA was performed on single cells using the Ampli1™ WGA kit version 02 (Menarini Silicon Biosystems, Bologna, IT) as per manufacturer instructions. For single cells derived from blood (CTCs and WBC), the quality of the WGA product was determined using the Ampli1™ QC Kit (Menarini Silicon Biosystems, Bologna, IT). A genomic integrity index (GII) was allocated for each sample scored from 0 to 4. Only single cells with sufficiently good quality DNA as determined by a GII ≥ 2 were selected for downstream analysis.

Low-pass whole genome sequencing and bioinformatics

Ampli1™ low-pass kit for Illumina (Menarini Silicon Biosystems, Bologna, IT) was used for preparing low-pass Whole Genome Sequencing (lpWGS) libraries from single cells. Forhigh-throughput processing, the manufacturer procedure was implemented in a fully automated workflow on Ion Torrent Ion S5-system (ThermoFisher, Waltham, MA, USA). Ampli1™ low-pass libraries were normalized and sequenced by Ion530 chip. The obtained FASTQ files were quality checked and aligned to the hg19 human reference sequence using tmap aligner tool on Torrent_Suite 5.10.0. and alignment (BAM) files were generated. All samples with < 400.000 reads were excluded from the analyses.

BAM files underwent quality filtering using qualimap 30 and were processed using two separate pipelines for CIN and CNAs. Each chromosomal break between contiguous regions of at least 10 Mb was tabulated to calculate the number of large-scale transitions (LSTs) per CTC genome. Copy number alterations were identified using QDNAseq software (version 11.0) according to the following settings: minMapq = 37, window = 500 kb. “Gain” and “loss” calls were filtered out by residual (> 4 standard deviation, SD) and black list regions reported in ENCODE database. Segmented copy number data of each sample were extracted starting from log2Ratio value. For the purpose of CNA profile, chromosome 19 was not considered due to its biased deletion associated with the high CG base percentage. Samples were classified as aberrant if they exhibited either ≥ 1 genomic regions with amplification/deletion greater than 12.5 Mb, or if the cumulative amplification/deletion of different genomic regions exceeded 37.5 Mb. OncoKb database was interrogated to evaluate biological and clinical relevant CNAs in CTCs (access date: March 2024).

Biological analyses relied on canonical oncogenic signaling pathways, as previously defined 31 and processed using custom functions from the maftools R package 32 , alongside Gene Ontology (GO) biological process terms and KEGG pathways via the ClusterProfiler Bioconductor package. CIN predictor was developed using the SMOTE method 33 to address sample imbalance between presence and absence of LSTs. Classification was performed using the random forest algorithm on 39 genes 34 with bootstrap re-sampling used to estimate standard errors and confidence intervals. The discriminatory capability of the CIN classifier was assessed using ROC curves and expressed by AUC values. Analyses of association were conducted using t-test for continuous variables, and Fisher test for categorical variables. All analyses were performed using R software ( www.R-project.org ), statistical significance was set at a p-value < 0.05.

Conference presentation

These results have been presented in part at the Molecular Analysis for Precision Oncology (MAP) Congress, Amsterdam, Netherlands, Oct 14–16, 2022.

Data availability

Raw sequencing data are available from the corresponding author upon request.

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Acknowledgements

We acknowledge the skilful technical support by Patrizia Miodini and Rosita Motta for CTC enrichment.

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Serena Di Cosimo, Marco Silvestri, Cinzia De Marco & Vera Cappelletti

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Alessia Calzoni

Department of Radiation Oncology, Fondazione IRCCS Istituto Nazionale Dei Tumori Di Milano, Milan, Italy

Maria Carmen De Santis & Maria Grazia Carnevale

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Contributions

Conceptualization: S.D.C., M.S., V.C.; Sample collection and processing: M.C.D.S., M.G.C., C.D.M., C.R.; Data curation and analysis: M.S., V.C., C.R., A.C., S.D.C.; Writing: S.D.C.; V.C., Supervision: S.D.C., V.C., M.C. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Marco Silvestri .

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The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Fondazione IRCCS Istituto Nazionale dei Tumori di Milano (INT 196/14).

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Informed consent was obtained from all study participants.

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Di Cosimo, S., Silvestri, M., De Marco, C. et al. Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer. Sci Rep 14 , 20479 (2024). https://doi.org/10.1038/s41598-024-71378-3

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Distributions for sex (A), decedents (B), race and ethnicity (C), and dual-eligibility (D). Beneficiary race and ethnicity was determined using the Research Triangle Institute race code; Other and unknown race and ethnicity category includes Asian and Pacific Islander, American Indian or Alaska Native, and any race or ethnicity not otherwise specified. ASR indicates age-standardized rate.

eAppendix. Literature Review Protocol

eTable 1.  ICD-10-CM Codes and Prescription Drugs Used in the CCW and 21 Unique Researcher-Developed Claims-Based Dementia Identification Algorithms

eTable 2. Characteristics of Beneficiaries Categorized Into Each Tier of ICD-10-CM Codes (as Defined by Frequency of Use Across the CCW and Researcher-Developed Algorithms) and NDCs

eTable 3 . Raw and Age-Adjusted Characteristics of Beneficiaries Identified as Having Highly Likely ADRD, Likely ADRD, Possible ADRD, and No Evidence of ADRD

eTable 4. Beneficiary Age Distribution in the Full Sample, Within LTC Users and Non-Users, and Within Decedents and Non-Decedents

eTable 5. New Subcodes Associated With F01, F02, and F03 That Went Into Effect in October 2022

eReferences

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Gianattasio KZ , Wachsmuth J , Murphy R, et al. Case Definition for Diagnosed Alzheimer Disease and Related Dementias in Medicare. JAMA Netw Open. 2024;7(9):e2427610. doi:10.1001/jamanetworkopen.2024.27610

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Case Definition for Diagnosed Alzheimer Disease and Related Dementias in Medicare

• 1 NORC at the University of Chicago, Bethesda, Maryland
• 2 Department of Epidemiology, George Washington University School of Public Health, Washington, DC

Question   How many Medicare beneficiaries have diagnostic codes or drug prescriptions indicating Alzheimer disease and related dementias (ADRD) using a refined case definition, and what are the characteristics of these beneficiaries?

Findings   This cross-sectional study of more than 60 million Medicare beneficiaries identified 7.2% with evidence of highly likely ADRD, 1.9% with likely ADRD, and 4.3% with possible ADRD. Beneficiaries with evidence of ADRD were older, more frail, more likely to use long-term care, and more likely to die than those without evidence of ADRD; these differences persisted after age-standardization.

Meaning   In this cross-sectional study, more than 5.4 million Medicare beneficiaries (9.1%) had evidence of likely or highly likely ADRD in 2019; pending validation, this case definition can be adopted provisionally for national surveillance of persons with diagnosed dementia in the Medicare system.

Importance   Lack of a US dementia surveillance system hinders efforts to support and address disparities among persons living with Alzheimer disease and related dementias (ADRD).

Objective   To review diagnosis and prescription drug code ADRD identification algorithms to develop and implement case definitions for national surveillance.

Design, Setting, and Participants   In this cross-sectional study, a systematic literature review was conducted to identify unique International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and prescription drug codes used by researchers to identify ADRD in administrative records. Code frequency of use, characteristics of beneficiaries identified by codes, and expert and author consensus around code definitions informed code placement into categories indicating highly likely, likely, and possible ADRD. These definitions were applied cross-sectionally to 2017 to 2019 Medicare fee-for-service (FFS) claims and Medicare Advantage (MA) encounter data to classify January 2019 Medicare enrollees. Data analysis was conducted from September 2022 to March 2024.

Exposures   ICD-10-CM and national drug codes in FFS claims or MA encounters.

Main Outcomes and Measures   The primary outcome was counts and rates of beneficiaries meeting each case definition. Category-specific age, sex, race and ethnicity, MA enrollment, dual-eligibility, long-term care utilization, mortality, and rural residence distributions, as well as frailty scores and FFS monthly expenditures were also analyzed. Beneficiary characteristics were compared across categories, and age-standardized to minimize confounding by age.

Results   Of the 60 000 869 beneficiaries included (50 853 806 aged 65 years or older [84.8%]; 32 567 891 female [54.3%]; 5 555 571 Hispanic [9.3%]; 6 318 194 non-Hispanic Black [10.5%]; 44 384 980 non-Hispanic White [74.0%]), there were 4 312 496 (7.2%) with highly likely ADRD, 1 124 080 (1.9%) with likely ADRD, and 2 572 176 (4.3%) with possible ADRD, totaling more than 8.0 million with diagnostic evidence of at least possible ADRD. These beneficiaries were older, more frail, more likely to be female, more likely to be dual-eligible, more likely to use long-term care, and more likely to die in 2019 compared with beneficiaries with no evidence of ADRD. These differences became larger when moving from the possible ADRD group to the highly likely ADRD group. Mean (SD) FFS monthly spending was $2966 ($4921) among beneficiaries with highly likely ADRD compared with $936 ($2952) for beneficiaries with no evidence of ADRD. Differences persisted after age standardization.

Conclusions and Relevance   This cross-sectional study of 2019 Medicare beneficiaries identified more than 5.4 million Medicare beneficiaries with evidence of at least likely ADRD in 2019 using the diagnostic case definition. Pending validation against clinical and other methods of ascertainment, this approach can be adopted provisionally for national surveillance.

Surveillance is a fundamental public health activity. Lack of a US dementia surveillance system hinders public health efforts to support persons living with Alzheimer disease and related dementias (ADRD), address health disparities, and plan ADRD care resources.

Medicare administrative data are an attractive source upon which to build a dementia surveillance system and are commonly used to identify persons living with ADRD, but a consensus diagnostic code case definition does not exist. Perhaps the most widely used definition (the Centers for Medicare and Medicaid Services [CMS] Chronic Conditions Warehouse [CCW] algorithm) uses 22 International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes, from the commonly accepted G30.X (Alzheimer disease) and F01.XX (vascular dementia), to less specific codes such as R54 (age-related physical debility). 1 In contrast, most researcher-developed ICD-10 - CM –based algorithms exclude R54, but may include codes such as G31.0 (frontotemporal dementia) that are not in the CCW algorithm. 2 - 4 Moreover, while some algorithms use Medicare Part D data to identify prescriptions for Alzheimer disease–related drugs, 5 - 8 most do not.

The impact of using different ICD-10-CM or prescription codes on the number of people identified or their characteristics is unknown. Because ICD-10-CM codes are used for billing (rather than diagnostic) purposes, specific codes may not be sensitive nor specific to dementia, and coding practices may differ systematically by health care practice, patient characteristics, and geography.

We examined how choices of ICD-10-CM and prescription drug codes used to identify persons with clinically recognized ADRD in Medicare fee-for-service (FFS) claims and Medicare Advantage (MA) encounter data affect dementia prevalence estimates and characteristics of the people identified. We synthesized this information to develop a new case definition using diagnostic and prescription drug codes that can be applied to administrative data to support surveillance of persons with diagnosed dementia in the Medicare system.

This cross-sectional study was deemed exempt from review and the requirement of informed consent by the NORC Institutional Review Board. The reporting of this research follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We searched PubMed for articles published from 2012 to 2022, with all-cause dementia or ADRD as a primary exposure or primary outcome, or where the research population of interest was persons living with all-cause dementia or ADRD (eAppendix in Supplement 1 ). We found 28 studies utilizing 20 distinct researcher-developed ICD-10-CM or prescription drug code algorithms in addition to the CCW algorithm (eTable 1 in Supplement 1 ). 2 - 21

We extracted 43 ICD-10-CM codes and 5 prescription drugs across algorithms ( Table 1 and eTable 1 in Supplement 1 ). We shared the codes with 3 clinicians (2 neurology clinicians and 1 geriatrics clinician) who provide care to persons living with dementia, who recommended excluding 8 codes deemed to not indicate dementia ( Table 1 ). We grouped the remaining codes into tiers by use frequency (tier 1, ≥15 algorithms; tier 2, 10-14 algorithms; tier 3, 5-9 algorithms; and tier 4, 1-4 algorithms). We designated prescriptions for ADRD-targeting drugs as indicated by National Drug Codes (NDC) without presence of an ADRD ICD-10-CM code as tier 5.

We used 100% of the 2017 to 2019 Medicare FFS inpatient, outpatient, carrier, skilled nursing facility, home health agency, and hospice claims; MA inpatient, outpatient, carrier, skilled nursing facility, and home health agency encounter data; and Medicare Part D prescription drug event (PDE) data. We used the minimum dataset (MDS 3.0) to identify long-term care (LTC) utilization. We limited analysis to Medicare beneficiaries with at least Part A (the premium-free Medicare benefit) enrollment in January 2019, nonmissing sex, and a valid US state or territory code based on the Medicare beneficiary summary files. We did not exclude beneficiaries based on age or lack of Part B enrollment because our aim was to identify all people in the Medicare system with evidence of ADRD.

To categorize beneficiaries with or without evidence of dementia as of 2019, we conducted a cross-sectional analysis of January 2017 to December 2019 FFS and MA data to identify all claims and encounters with a relevant ICD-10-CM code listed in any position, and all PDE claims for a relevant NDC. We classified beneficiaries hierarchically, first with a tier 1 ICD-10-CM code, then with a tier 2 code among remaining beneficiaries, and so on, identifying only the incremental beneficiaries in each tier if they had not been classified earlier. We compared distributions of age, sex, race and ethnicity (as indicated by the Research Triangle Institute race code 22 ), MA enrollment, LTC use, and 2019 mortality across tiers. Race and ethnicity categories included American Indian or Alaska Native, Asian or Pacific Islander, Hispanic, non-Hispanic Black, non-Hispanic White, unknown, and other (defined as any race or ethnicity not otherwise specified); race and ethnicity were included because existing evidence shows that there are disparities in dementia prevalence across race and ethnicity groups. We compared cross-tier beneficiary frailty using a claims-based frailty index (CFI), 23 an adapted CFI that excludes ADRD codes in tiers 1 to 4, and per-member-per-month (PMPM) spending, averaged across all months of 2019 FFS coverage.

Using these data (eTable 2 in Supplement 1 ), we found that beneficiaries in tiers 1 and 2 were older, more frail, more likely to be female, in LTC, and die than those in tiers 3 to 5. There were minimal differences in race and ethnicity across tiers, with exception of a higher-than-expected representation of Hispanic and Asian and Pacific Islander beneficiaries in tier 5; however, the overall size of the sample categorized as tier 5 was very small, at just 0.1% (52 338 of 60 000 869 beneficiaries). Based on the findings from the cross-tier comparison and author consensus, we further aggregated codes into 3 categories with decreasing confidence of having a true ADRD diagnosis: a highly likely ADRD category requiring at least 2 claims or encounters on different dates with ICD-10-CM codes from tiers 1 or 2; a likely ADRD category requiring 1 claim or encounter with an ICD-10-10-CM code from tiers 1 or 2; and a possible ADRD category requiring at least 1 claim or encounter with an ICD-10-CM or NDC code from tiers 3, 4, or 5 over a 3-year lookback period. We categorized beneficiaries and reevaluated group demographics, health insurance type, frailty and mortality, and rural residency. We then computed prevalence of highly likely, likely, and possible ADRD within population subgroups defined by these characteristics. We age-standardized to the full analytical population to evaluate differences unconfounded by age.

All analyses were conducted in SAS Enterprise Guide 7.1 and SAS Studio version 3.81 (SAS Institute). Data analysis was conducted from September 2022 to March 2024.

Of 64 430 729 2019 Medicare beneficiaries, we excluded 3 940 831 due to lack of Part A enrollment in January, 8 due to missing sex, and 489 021 due to a nonvalid US state or territory code, resulting in a total of 60 000 869 beneficiaries (50 853 806 aged 65 years or older [84.8%]; 32 567 891 female [54.3%]; 5 555 571 Hispanic [9.3%]; 6 318 194 non-Hispanic Black [10.5%]; 44 384 980 non-Hispanic White [74.0%]) included in the study sample. Of all beneficiaries, 11 502 479 (19.2%) had Medicaid dual-eligibility, while 23 607 426 (39.3%) had MA. Mean (SD) FFS PMPM spending in 2019 was $1220 ($3426) ( Table 2 ).

We identified 4 312 496 beneficiaries (7.2%) as having highly likely ADRD, and 1 124 080 (1.9%) as having likely ADRD ( Table 3 ). The proportion of beneficiaries with highly likely ADRD increased to 8.1% (4 125 639 beneficiaries) after limiting age to 65 years or older, and to 8.8% (4 093 008 beneficiaries) when further limiting to those with both Parts A and B enrollment. The proportion of beneficiaries with likely ADRD increased to 2.1% (996 379 beneficiaries) after these restrictions. Compared with those with likely ADRD, those with highly likely ADRD were older and more frail, more likely to be female and dual-eligible, had over 3 times the rate of LTC utilization (681 923 of 4 312 496 beneficiaries [15.8%] vs 51 332 of 1 124 080 beneficiaries [4.6%]), and almost double the rate of death (828 366 of 4 312 496 beneficiaries [19.2%] vs 129 705 of 1 124 080 beneficiaries [11.5%]). We identified an additional 2 572 176 beneficiaries (4.3%) as having possible ADRD; this percentage increased to 4.8% (2 231 673 beneficiaries) after restricting to beneficiaries aged 65 years or older with Parts A and B enrollment. The possible ADRD group was younger and healthier (lower CFI, mortality, and LTC utilization) than those with highly likely or likely ADRD but was older and less healthy than those with no evidence of ADRD (51 992 117 beneficiaries). Mean (SD) PMPM spending was approximately 3 times as high in the ADRD groups (ranging from $2559 [$2952] among those with possible ADRD to $2966 [$4921] among those with highly likely ADRD) as that of the no ADRD group ($936 [$2952]). Age standardization narrowed differences in sex distribution and death rates, widened differences in race and ethnicity distribution and dual-eligible rates, and had minimal impact on differences in MA enrollment, LTC utilization, and frailty ( Figure and eTable 3 in Supplement 1 ). FFS spending increased slightly for all categories after age standardization.

The proportion of beneficiaries with any evidence of ADRD increased with age, from 6.5% (1 931 517 of 29 878 739 beneficiaries) among beneficiaries aged 65 to 74 years to 42.5% (2 544 205 of 5 983 967) among those aged 85 years or older, with the largest increase seen in the percentage of those with highly likely ADRD (2.6% [770 296 of 29 878 739 beneficiaries] to 29.1% [1 739 705 of 5 983 967 beneficiaries]) ( Table 4 ). Prevalence of any ADRD was higher in females than in males but was similar between non-Hispanic White (5 950 598 beneficiaries [13.4%]), non-Hispanic Black (892 541 beneficiaries [14.1%]), and Hispanic (792 948 beneficiaries [14.3%]) beneficiaries. Those with LTC use were substantially more likely to have ADRD than those with no LTC (681 923 of 937 248 beneficiaries [72.8%] vs 3 630 573 of 59 063 621 beneficiaries [6.1%] categorized as highly likely). Similarly, prevalence of highly likely or likely ADRD was much higher in decedents (958 071 of 2 285 257 beneficiaries [41.9%]) than nondecedents (4 478 505 of 57 715 612 beneficiaries [7.7%]) and in those who were dual-eligible (1 917 434 of 11 502 479 beneficiaries [16.7%]) than among those who were not (3 519 142 of 48 498 390 beneficiaries [7.2%]). MA beneficiaries had a higher prevalence of highly likely or likely ADRD (2 296 154 of 23 607 426 beneficiaries [9.7%]) than FFS beneficiaries (3 140 422 of 36 393 443 beneficiaries [8.6%]), and any evidence of ADRD (4 595 211 of 23 607 426 beneficiaries [14.5%] for MA vs 4 593 211 of 36 393 443 beneficiaries [12.6%] for FFS).

Age-standardizing subgroups to the age distribution of the Medicare population resulted in changes in ADRD prevalence estimates in some groups ( Table 4 ). Relative differences in ADRD prevalence narrowed across sex but widened across race and ethnicity groups. Most notably, non-Hispanic White beneficiaries became less likely to have any evidence of ADRD (12.9% across categories), while racial and ethnic minority groups became more likely to have evidence of ADRD (non-Hispanic Black beneficiaries, 16.5%; Hispanic beneficiaries, 15.3%). Among non-Hispanic Black beneficiaries, age standardization resulted in a substantial increase in the proportion of those with highly likely or likely ADRD (9.9% to 12.0%). Age standardization also reduced ADRD prevalence among LTC users (from 72.8% to 62.8% with highly likely ADRD) and decedents (from 36.2% to 23.8% with highly likely ADRD) but had minimal impact in ADRD prevalence among non–LTC users and nondecedents; this is because LTC-users and decedent groups were heavily skewed toward older ages, while the age distribution of the non–LTC users and nondecedent groups mimicked that of the general Medicare population (eTable 4 in Supplement 1 ).

Among 2019 Medicare beneficiaries in this cross-sectional study, we identified approximately 4.3 million (7.2%) with highly likely ADRD, 1.1 million (1.9%) with likely ADRD, and 2.6 million (4.3%) with possible ADRD, for a total of more than 8.0 million (13.4%) in any category. Specifically, we developed new diagnosis and NDC code ADRD case definitions informed by a systematic review of previous algorithms, author and expert input, and analyses of Medicare data. The review identified 43 ICD-10-CM codes and 5 prescription drugs used by the CCW and 20 researcher-developed algorithms to identify ADRD in Medicare data. We divided codes into categories that were likely to indicate ADRD vs those that were possibly ADRD based on past frequency of use by other researchers, characteristics of beneficiaries identified by codes, and author and expert consensus around code definitions. We then added a highly likely category to describe beneficiaries who received 2 or more likely codes on different dates of service. We posit that these categories are superior to previous definitions for provisional use in surveillance systems, but caution that validation is necessary. To our knowledge, this is the first application of claims identification algorithms to all-age FFS and MA beneficiaries. We have used this case definition to compute provisional national-, state-, and county-level estimates of ADRD prevalence and incidence in 2020 Medicare and published them on our dementia surveillance website. 24 Estimates will be refined pending validation and updated with additional years of data as they become available.

Our 3-level case definition is novel in that it was driven by researcher-consensus as well as data analysis and identifies dementia with varying degrees of certainty. Of note, ICD-10-CM codes used to identify possible ADRD have lower researcher consensus and less specific code descriptions (ie, do not contain dementia or Alzheimer ). Use of the possible ADRD codes may reflect physician uncertainty about a dementia diagnosis or medical events involving ADRD-like symptoms in patients without underlying dementia. 25 - 27 Our definition also excludes several previously used codes that were determined to not indicate ADRD by expert clinicians. Compared with the commonly used CCW algorithm, which similarly uses a 3-year look-back period, our case definition is more specific when limited to the highly likely and likely categories, but broader when also including the possible ADRD category. The CCW algorithm estimated prevalence of 10.7% in 2019 Medicare FFS beneficiaries 28 falls between our estimates for FFS beneficiaries of 8.6% for highly likely or likely ADRD and 12.6% for all 3 categories.

Importantly, we saw expected and meaningful differences between beneficiaries identified in each ADRD category. Moving from the no evidence of ADRD to the highly likely ADRD groups, beneficiaries became progressively older and more frail and had greater rates of dual-eligibility, LTC use, and death, which is consistent with prior research. 29 - 35 Notably, prevalence of highly likely ADRD was 29.1% in beneficiaries aged 85 years or older, 72.8% in LTC users, and 36.2% in decedents, compared with 7.2% in the general Medicare population. Higher rates of dual-eligibility in ADRD groups may be driven by ADRD beneficiaries spending down assets to qualify for Medicaid and obtain LTC coverage. These differences persisted after age standardization and lend confidence to our case definitions.

Application of our case definitions also showed disparities in diagnosis rates by race in the expected direction—higher dementia risk among non-Hispanic Black beneficiaries relative to non-Hispanic White beneficiaries 36 , 37 —after age standardization to account for lower life expectancy among non-Hispanic Black individuals. 38 However, because non-Hispanic Black individuals also have a greater risk of under-diagnosis of ADRD than non-Hispanic White individuals, 39 disparities in true underlying rates may be higher than observed. Additionally, we found higher-than-expected representation of Hispanic and Asian and Pacific Islander beneficiaries among those that had an ADRD-targeting drug without diagnostic ( ICD-10-CM ) evidence. We hypothesize that differences in cultural perceptions around dementia and cognitive decline (eg, memory loss as a normal aging process) 40 , 41 may result in lower utilization of diagnosis codes when providers suspect dementia. Using PDE claims may result in higher and more accurate rates of ADRD among Hispanic and Asian and Pacific Islander individuals despite the overall small number of beneficiaries identified by PDE claims alone.

Finally, also consistent with past research, 29 , 35 , 42 PMPM FFS spending was substantially higher for beneficiaries with evidence of ADRD compared with those with no evidence of ADRD. Medicare FFS PMPM spending was relatively similar across the highly likely, likely, and possible ADRD groups despite differences in frailty and mortality. Medicare FFS spending may not be generalizable to those with MA (for whom costs cannot be computed) and is only part of the economic story. Medicaid is the primary US payer of LTC; higher rates of dual-eligibility and LTC use among the highly likely ADRD group indicate that differences in total federal and state spending between the highly likely ADRD and other groups are likely larger. We also did not capture patient and family health–related out-of-pocket expenses and informal care costs ($203 117 in families caring for a patient living with dementia vs $102 955 in families caring for a patient without dementia over the last 7 years of the patient’s life 42 ), forgone wages, or other impacts on informal caregivers, and payments made by other assistance programs. Finally, we caution that our spending measure represents total Medicare FFS spending, rather than the incremental ADRD costs.

This study is limited by at least the following. First, our ADRD case definition was driven by researcher-consensus, and validation against other dementia ascertainment methods (including ascertainment based on in-person clinical and neuropsychological assessments) is necessary. Both over- and under-diagnosis of ADRD have been documented in Medicare claims, 35 , 39 and the 8.0 million beneficiaries identified as having some evidence of ADRD by our case definition will include some without ADRD, especially those in the possible category. Similarly, this method only captures documented cases of dementia in Medicare administrative records and cannot capture beneficiaries with unrecognized and/or undocumented ADRD. If we assume a 60% rate of undetected dementia in the US 43 our estimates would suggest an additional 12 million beneficiaries may be living with ADRD. Additionally, our data show a marginally higher rate of ADRD in MA than in FFS enrollees (14.5% vs 12.6% across the 3 categories), which may reflect beneficiary selection in MA plans, MA vs FFS differences in clinical ADRD assessment and diagnosis rates, differences in claims or encounter documentation, or a combination thereof. Given the rapid rise in MA participation (from 33% in 2017 to 51% in 2023) and variation in MA penetration across counties, 44 , 45 it is also important to understand potential differences in performance of this case definition between MA and FFS beneficiaries. As such, validation of this case definition against in-person clinical and other ascertainment methods to assess performance (including sensitivity, specificity, positive predictive value, and negative predictive value), separately for Medicare FFS and MA, is critical for refining and calibrating estimates to accurately capture the diagnosed prevalence and incidence of dementia. Pending validation, our case definitions should be considered provisional. Notably, we expect the possible ADRD category to identify a higher proportion of individuals who do not have ADRD. Thus, it is important to report the possible ADRD category separately from the likely and highly likely ADRD categories in research and surveillance efforts using these case definitions.

Second, evidence for ADRD documented in electronic health or insurance records outside the Medicare system is not captured by our method; this is particularly problematic for beneficiaries without Medicare Parts B or D (7.5% and 25.6% of Medicare enrollees, respectively 43 , 46 ). Third, we deliberately used data from 2017 to 2019 to avoid the COVID-19 pandemic years, which resulted in secular shocks, including excess senior deaths, forgone or deferred care, and increased telehealth, which may have impacted dementia diagnosis. Research is necessary to understand these effects but will necessarily be delayed pending new data. Fourth, Namzaric, a memantine and donepezil combination drug approved in 2014, was not included by any prescription-drug based identification strategy; while the impact of including this drug necessitates further investigation, we anticipate a negligible effect given that just 0.1% of the sample had an ADRD-targeting prescription drug without ICD-10-CM evidence. Similarly, ICD-10 -CM code updates from October 2022 added 29 highly specific codes each under code roots F01 (vascular dementia) F02 (dementia in other diseases classified elsewhere), and F03 (unspecified dementia) (eTable 5 in Supplement 1 ). 47 We recommend that applications of our approach to Medicare records beginning in October 2022 include these for identifying highly likely and likely ADRD. Fifth, in developing our case definitions, we only considered use of ICD-10-CM codes and prescription drugs but did not consider other criteria of existing ADRD-identification algorithms, including look-back period, types of claims or encounter data considered, number of claims or encounters with relevant ICD-10-CM codes required, and time elapsed between claims and encounters; sensitivity analyses around these different criteria are beyond the scope of this paper.

In this cross-sectional study, our novel case definition for ADRD identified approximately 5.4 million Medicare beneficiaries with evidence of at least likely ADRD in 2019. Pending validation against in-person clinical and other ascertainment methods, this definition can be adopted for provisional use in national surveillance efforts.

Accepted for Publication: June 17, 2024.

Published: September 3, 2024. doi:10.1001/jamanetworkopen.2024.27610

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Gianattasio KZ et al. JAMA Network Open .

Corresponding Author: Kan Z. Gianattasio, PhD, NORC at the University of Chicago, 4350 East-West Hwy 8th Floor, Bethesda, MD 20814 ( [email protected] ).

Author Contributions: Mr Wachsmuth and Mr Murphy had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Gianattasio, Hartzman, Wittenborn, Power, Rein.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Gianattasio, Wachsmuth, Hartzman, Cutroneo, Wittenborn, Rein.

Critical review of the manuscript for important intellectual content: Gianattasio, Murphy, Hartzman, Montazer, Power, Rein.

Statistical analysis: Gianattasio, Wachsmuth, Murphy.

Obtained funding: Wittenborn, Rein.

Administrative, technical, or material support: Hartzman, Montazer, Cutroneo.

Supervision: Hartzman, Rein.

Conflict of Interest Disclosures: None reported.

Funding/Support: Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health (Award No. R01AG075730).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We would like to thank Christina Prather, MD (George Washington University); Tania Alchalabi, MD (George Washington University); and Raymond Scott Turner, MD, PhD (Georgetown University), for providing critical review of the International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes from a clinical perspective. Drs Prather, Alchalabi, and Turner did not receive compensation for their contributions to this work. We also wish to acknowledge the critical input into data processing and analysis decisions made by Qian Gu, PhD (KPMG); Carrie Bao, BS (KPMG); and Samuel Knisely, BA, (KPMG). Dr Gu, Ms Bao, and Mr Knisely received funding from the same National Institute on Aging grant (R01AG075730) that funded this study for their input.

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BMC Nursing volume  23 , Article number:  614 ( 2024 ) Cite this article

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Today, the human population faces an increasing array of emerging environmental challenges. Despite its importance, nurses often neglect ecological issues, which can compromise patient health. While the ecological nursing perspective has the potential to lead to innovative care approaches that benefit patients, the nursing profession, and the environment, the concept of ecological care lacks a clear definition and its dimensions remain unclear. This study aimed to analyze and clarify the concept of ‘ecological care’ in the nursing discipline.

Walker and Avant’s analysis method was used to identify descriptions, antecedents, consequences, and empirical referents of the concept of ‘ecological care’ in nursing. We searched the databases (PubMed, Scopus, PsycINFO, CINAHL, ERIC, SID, and IranDoc) using the keywords “ecological,” “nurse,” and “nursing” using Boolean operators “AND” and “OR” in the title and abstract fields both in English and Persian to identify relevant literature on ecological care in nursing.

Ecological care, as a multidimensional concept, encompasses ecological thinking, ecological attitude, ecological awareness, ecological sensitivity, and ecological literacy. This entails the optimal utilization of environmental factors to provide patients with high-quality care and preserve ecological sustainability through environmentally friendly behaviors.

Conclusions

The findings highlight the need to elucidate, endorse, and solidify ecological thinking in all aspects of nursing care including nursing management, education, and research, which can lead to improved care quality, patient safety, and sustainability. Within this framework, nursing educators could play an essential role in integrating ecological care into nursing education. The study emphasizes the need to integrate ecological thinking into all aspects of nursing.

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Ecology, the study of interactions between living organisms and their environments, encompasses physical and social surroundings that impact all living beings. From a human science perspective, ecology emphasizes these interconnected relationships, fostering a deeper understanding of nursing and caring practices [ 1 ]. Currently, environmental concerns are considered significant threats to public health. However, healthcare professionals often lack sufficient awareness of the importance of ecological issues [ 2 ].

As the largest group of healthcare professionals, nurses play a crucial role in decisions regarding product use, energy consumption, and chemical selection in healthcare settings. However, they face a significant challenge: balancing environmental concerns and ecological principles with their professional duties [ 3 ]. Although nurses can advocate reducing exposure to harmful chemicals and adopting less toxic products, their work environments often require high energy consumption and generate substantial medical waste [ 4 ]. This medical waste encompasses both hazardous (infectious, pathological, chemical, pharmaceutical, cytotoxic, and radioactive) and non-hazardous or general waste, posing potential risks to patients, communities, and broader ecological health [ 5 ]. Multiple studies have highlighted the critical role of ecological considerations within healthcare in the overall health of ecosystems [ 6 , 7 , 8 , 9 ]. Consequently, ecological issues have become a high priority for nurses, demanding attention and action [ 10 ].

The importance of environment, ecosystems, and ecology in nursing practice has been recognized by pioneers like Florence Nightingale as the founder of modern nursing (published in 1992, originally written in 1959) [ 11 ] and subsequently by Fawcett (1984) [ 12 ]. This vision is further reflected in the International Council of Nurses (ICN) Code of Ethics, which states that “nurses contribute to the population’s health and work to achieve the sustainable development goals.” By adopting sustainable practices, nurses can significantly reduce their environmental footprint and contribute to achieving the UN 2030 Agenda for Sustainable Development [ 9 ]. Recognizing this crucial role, nursing organizations such as the American Nurses Association actively promote nurses’ participation in environmental protection initiatives [ 13 ].

The concept of ecological care in nursing, as a multidimensional concept, encompasses several aspects. Lausten (2006) proposed a nursing ecological theory to broaden nurses’ perspectives by incorporating concepts of global ecosystems, communities, and interrelationships from the ecological sciences. This theory recognizes that human interactions with the environment extend beyond the personal sphere and encompass professional activities. Consequently, nurses can integrate ecological principles into their practice, fostering new directions in care that benefit patients, healthcare professionals, and the environment [ 14 ]. Dahlberg et al. (2016) conducted an empirical study to explore how a phenomenological life-world theory could expand the concept of holistic care into “ecological care.” They argued that the traditional approach to holistic care has neglected environmental and ecological dimensions. Their findings suggested that ecological care goes beyond fighting illnesses. It emphasizes understanding patients within the context of their world, a world that they both influence and are influenced by. This approach helps patients reintegrate into their rhythm of existence [ 1 ].

Al-Shamaly (2021) highlights “ecological awareness,” which emphasizes creating a safe and comfortable patient environment through noise, light, color, and temperature control [ 15 ]. Sattler (2013) adds another dimension, suggesting that nurses can act as catalysts for transforming hospitals into environmentally sustainable spaces. This can be achieved through practices such as adopting environmentally friendly purchasing policies (e.g., waste management strategies, reduced chemical use, and proper disposal of hazardous materials such as batteries), promoting healthy food options, and favoring mercury-free products [ 16 ].

Although ecological factors could influence the quality of care, patient safety, individual and community health, resource preservation, and sustainable practices [ 16 , 17 , 18 , 19 ], nurses’ awareness of ecological care and its dimensions remains limited [ 2 ]. Moreover, there is no universally accepted definition of ecological care as a complex concept [ 20 ]. Therefore, this study aimed to analyze and clarify the concept of ‘ecological care’ within the nursing discipline.

Walker and Avant’s concept analysis method was used as a rigorous and systematic approach to identify descriptions, antecedents, consequences, and empirical referents of the concept of ‘ecological care’ in nursing. Ecological care is a widely applicable concept that extends beyond the confines of nursing care. Therefore, the literature review encompasses all the various applications of ecological care, including both implicit and explicit aspects. The stages of the concept analysis method are as follows: (A) selecting a concept, (B) determining the aims or purposes of the analysis, (C) identifying all uses of the concept that you can discover, (D) determining the defining attributes, (E) identifying a model case, (F) identifying borderline, related, contrary, invented, and illegitimate cases, (G) identifying antecedents and consequences, and (H) defining empirical referents [ 21 ].

Literature search

A systematic literature review was conducted using multiple health databases, including PubMed, Scopus, PsycINFO, CINAHL, ERIC, SID, and IranDoc. The concepts “ecological,” “nurse” and “nursing” were searched using Boolean operators “AND” and “OR” in the title and abstract fields of each database. No temporal limits were applied and articles published in either English or Persian until July 2023 were retrieved.

Initially, 1083 records were identified by searching the titles and abstracts of these databases. Subsequently, 16 additional records were manually included, resulting in a total of 1099 records. Duplicate records were removed, leading to an initial selection of 1068 records. The titles and abstracts of these records were screened, and the eligibility criteria were applied to the full text of the selected records. Eventually, 36 records met the criteria and underwent a comprehensive review of concept analysis (Fig.  1 ). A detailed overview of the included studies, including publication year, title, country, and key findings, can be found in Appendix A.

Flow diagram of the study (data search and selection process)

Concept selection

The importance of a specific concept is influenced by a variety of factors both within and outside its field over time. Consequently, concepts lacking clear definitions warrant further analysis [ 21 ]. Considering the interconnectedness of ecosystems and human health, as well as the imperative to maintain environmental sustainability, particularly within healthcare, the concept of ecology has gained prominence in nursing and other health professions. Nightingale’s emphasis on the environment underscores this importance. Given the increasing significance of ecological care in healthcare and the lack of a clear, unified definition, this concept was selected for analysis to elucidate its dimensions and characteristics.

Determining the aims of the analysis

The concept of “ecological care” has been insufficiently analyzed within the healthcare context, resulting in a lack of a clear definition. This study aims to refine the meaning of ecological care in nursing by identifying its descriptions, antecedents, consequences, and empirical referents.

Identifying the use of the concept

To explore the concept of ecological care, it is crucial to understand the distinct meanings of each word from a variety of sources such as dictionaries, thesauruses, websites, and scholarly literature.

According to the Merriam-Webster dictionary, the term ‘ecological’ is an adjective related to the science of ecology. This refers to the environment of living things or the relationships between living things and their environments [ 22 ].

According to the Merriam-Webster dictionary, the term ‘care’ functions both as a noun, representing responsibility for or attention to health, well-being, safety, or solicitude, and as a verb, meaning to feel interest or concern and to provide care [ 23 ].

Ecological care in nursing literature

The concept of ecological care, originating from the theory of biological ecology, aims to offer solutions that effectively minimize the adverse impacts of nursing care on the ecosystem [ 14 ]. Ecological care can be classified into two types: individuals and professionals. The individual approach focuses on raising public awareness, shaping attitudes and behaviors, and promoting responsible actions regarding energy consumption, the production of toxic substances (such as greenhouse gases), chemical usage, and healthy and organic diet adoption. Conversely, the professional approach emphasizes the importance of sensitivity, awareness, attitude, behavior, and responsible actions among individuals when carrying out their professional responsibilities [ 9 , 24 ].

Clinical environments require ecological care, which can be achieved through two distinct approaches: environmental and organizational care. Environmental care involves maintaining equipment and machines, ensuring workplace safety, minimizing risks, managing noise levels, optimizing lighting conditions, regulating temperature, and employing creative designs to create a comfortable and relaxing environment. It also involves facilitating visits from family members and pets and improving patients’ sleep quality. Additionally, the use of digital technology helps ensure a healthy and safe treatment environment for patients in the Intensive Care Units (ICU). On the other hand, organizational care focuses on time and resource management. This includes strategies such as reducing paper and ink consumption by utilizing electronic records, which aids in efficient time management. Organizational care aims to streamline nurses’ tasks and improve overall work efficiency by minimizing their workload and improving access to patient information. Finally, waste management practices play a crucial role in maintaining an environmentally conscious approach in healthcare settings [ 15 ].

Determining the defining attributes

Ecological thinking.

According to Balgopal and Wallace (2009), ecological thinking is a combination of ecological understanding and ecological awareness [ 25 ]. Understanding ecology involves understanding concepts such as biotic, abiotic, and biological interactions. This serves as the initial stage of ecological thinking, which is further developed by comprehending the impact of human activities on the ecosystem [ 26 ]. Ecological understanding can be conceptualized as a continuum, with one end representing the capacity to identify problems and propose ecological decisions, considering their potential consequences. On the other end of the continuum is a lack of understanding, where the ability to explain the impact of human actions on the ecosystem is insufficient [ 25 ].

Ecological thinking causes a transformation in people’s presuppositions and attitudes towards the surrounding world, enabling them to recognize that we are interconnected and evolving alongside nature. Embracing an ecological perspective requires acknowledging ourselves as integral components of nature rather than being superior to it. This encompassing concept embodies various underlying principles such as ecology, wholeness, interdependence, diversity, partnership, energy flows, flexibility, cycles, and sustainability [ 17 , 27 ]. Hes and de Plessis (2014) refer to this set of principles as the ‘ecological worldview.’ Shifting towards an ecological perspective entail altering our perspective on the world and ourselves. The fundamental essence of this transformation involves moving away from egocentric and anthropocentric thinking, which emphasizes separateness, and instead adopting a holistic perception that aims to counterbalance environmental damage. Enhancing ecological thinking can be achieved through the instruction of ecological concepts and behaviors [ 28 ].

Ecological attitude

Ecological attitude is a complex construct that encompasses various key components such as emotions, perceptions, personal norms, values, and relationships with the environment. The emotional dimension of ecological attitude plays a pivotal role in preparing individuals to address environmental issues and cultivate ecological behaviors in all aspects of life [ 29 , 30 , 31 ], as it determines the extent to which individuals will act in environmentally responsible ways [ 32 ].

Predicting a specific behavior entails possessing a specific attitude towards that behavior, as attitudes alone do not guarantee behavior, but predict or influence it [ 2 , 33 ]. Ecological behavior can be defined as the actions taken by a nurse to protect the environment, and it varies depending on the individual’s context and circumstances. Achieving the goal of ecological behavior can be challenging in certain situations, but it is crucial to promote sustainable living and preserve the planet’s natural resources [ 31 ].

Ecological awareness

Ecological awareness refers to knowledge, attitudes, and behaviors related to the environment. Its focus is on increasing responsibility toward achieving ecological sustainability [ 34 ]. One of its important characteristics is the perception of natural objects from a subject’s perspective [ 35 ]. As a theoretical and practical science, ecological awareness includes two stages: awareness of environmental changes, and feelings of concern about environmental problems and trying to solve them. People with ecological awareness try to be actively responsible for their interactions with the environment and exhibit positive behaviors towards the surrounding environment [ 9 , 20 ].

Ecological awareness is also a level of cognitive thinking that enables nurses to focus on protecting the environment while providing nursing care. This concept requires nurses to pay attention to the potential of nature and the surrounding environment that promotes, maintains, and restores human health [ 9 , 14 ]. This raises important questions about whether nurses are aware of the positive effects of recycling medical equipment and materials, or the harmful effects of greenhouse gases (CO2, NO, etc.) caused by fossil fuels and smoke from medical waste incinerators. It also highlights how much nurses are aware of the impact of their care activities on ecosystem damage and public health [ 9 , 19 , 36 ]. The role of nurses with ecological awareness is crucial in raising awareness among colleagues, managers, patients, and students [ 8 , 37 , 38 , 39 ].

Ecological sensitivity

Ecological sensitivity refers to the inclination to actively address environmental threats and the extent to which healthcare providers demonstrate awareness of hazardous and protective circumstances [ 40 ]. Individuals with varying psychological traits, such as extroversion or introversion, exhibit distinct levels of sensitivity to environmental health [ 41 ].

Ecological sensitivity is a multidimensional concept that contributes significantly to sustainable development. This serves as an emotional foundation for cultivating an ecological worldview and establishing personal norms for pro-environmental actions. This dynamic framework takes shape within families during childhood and is strengthened throughout professional life. Therefore, an essential initial step in enhancing ecological sensitivity among healthcare providers is to impart ecological education and raise awareness levels [ 42 , 43 , 44 ]. The development of ecological sensitivity is influenced by various factors, including families, educational institutions, mass media, and non-governmental organizations [ 45 , 46 , 47 ]. In general, nurses who actively engage in staying informed about ecological news and trends, participate in ecological protection activities and events, and demonstrate awareness of ecologically detrimental behaviors, both in themselves and their colleagues exhibit higher levels of ecological sensitivity [ 42 , 43 ].

Ecological literacy

Ecological literacy is a crucial concept that includes three core components: cognitive, emotional, and behavioral. According to UNESCO, there are five key characteristics of ecological literacy: awareness and sensitivity to the environment; comprehension of environmental issues; having values and sentiments towards environmental concerns; possessing skills, desire, and commitment; and actively engaging in identifying and resolving ecological problems. Generally, ecological literacy can be defined as the integration of environmental sensitivity, knowledge, skills, attitudes, values, responsibilities, and active engagement, which enables nurses to make informed and responsible decisions to promote environmental sustainability [ 48 , 49 ].

Model and additional cases

A model case serves as a paradigmatic illustration of the application of a concept encompassing all its defining elements. In addition to the model case, two other types of cases are presented: (A) the borderline case, which shares most of the essential characteristics of the concept but exhibits some differences; and (B) the contrary case, which presents an apparent example that contrasts with the concept, highlighting what it is not [ 21 ].

A 65-year-old woman was admitted to the neurology ward with a diagnosis of transient ischemic attack during the night shift. The attending nurse approached the patient’s bedside and introduced herself and the inpatient department. During the evaluation, the nurse observed the patients’ uneasiness, homesickness, and concerns regarding sleep disturbance due to changes in sleeping arrangements. She addressed the situation by repositioning the patient’s bed next to the window, aiming to provide a more comfortable environment and alleviate feelings of homesickness. Careful attention was paid to ensure that the bed and equipment were securely locked. During medication administration, the nurse utilized a tablet for dosage calculations, opting for a paperless approach to reduce waste. Proper disposal procedures were followed after medication administration, with empty vials discarded in the chemical waste bin, and needles placed in a safety box. During the initiation of infusion, the nurse noticed loose screws on the electronic infusion device and promptly sought assistance from a colleague to rectify the issue. Toward the end of her tasks, the nurse dimmed unnecessary lights in the ward and adjusted the alarm range of the device to an audible level for more comfort. Immediately before leaving the ward, the nurse noticed a leaking water tap and promptly contacted the facility manager to initiate immediate remedial action.

Borderline case

The head nurse of the pediatric ward conducted a clinical round when she heard the cries of a hospitalized 4-year-old child who was upset due to the absence of her cherished doll. Regrettably, the nurses disregarded the situation and continued down the corridor. Several months later, the nurse was invited to join a committee responsible for making decisions regarding hospital equipment procurement. Drawing from the recent knowledge acquired through a TV program highlighting the hazards of mercury to human health, she recommended the acquisition of mercury-free medical equipment.

Contrary case

A nurse, aged 35, with ten years of experience in surgery, approached the patient who had undergone laparotomy to perform a dressing change. The nurse inadvertently wore a pair of sterile gloves instead of non-sterile gloves while removing the contaminated dressing and disposed of it in the general waste bin. Subsequently, sterile gloves were replaced with a fresh pair, the wound was cleansed using six sterile gauzes, and an additional seven gauzes were applied to dress the surgical site, although a smaller quantity would have sufficed. During the hand washing process, the nurse’s mobile phone rang, and without turning off the water tap, he engaged in a conversation until the patient’s family intervened and turned off the tap. Finally, despite the patient expressing mild pain at the surgical site, the nurse chose to administer a painkiller instead of utilizing non-pharmacological methods to alleviate pain.

Identify antecedents and consequences

Walker and Avant (2011) provided a clear definition of antecedents as events or attributes that precede the occurrence of a concept, whereas consequences refer to events that ensue from the concept’s occurrence [ 21 ]. In this study, it was crucial to identify and examine the associated antecedents and consequences (Fig.  2 ). Therefore, the antecedents and consequences investigated are as follows:

Attributes, antecedents, and consequences of ecological caring in nursing practice

Antecedents

The ecological care provided by nurses can be influenced by both personal characteristics and organizational policies. Personal characteristics include creativity, innovation, responsibility, environmental friendliness [ 41 ], kindness, empathy, and strong communication skills [ 9 ]. Meanwhile, organizational policies encompass the establishment of a supportive organizational culture, provision of training courses [ 14 ], and design of a creative and humanitarian environment within hospitals and healthcare facilities. Moreover, ensuring a safe environment equipped with adequate resources, services, technology, and competent human resources is essential for delivering ecological care in therapeutic settings [ 15 ].

Consequences

Ecological care yields numerous benefits to patients, their families, healthcare providers, healthcare systems, and the environment. Among these benefits, one of the most significant is the provision of high-quality holistic care, which leads to increased patient satisfaction. Additionally, ecological care contributes to patient and staff safety by minimizing hospital infections, conserving energy (electricity, gases, and water), optimizing equipment and time utilization, reducing employee workload, managing hospital procurement costs, and eliminating hospital waste. It also plays a vital role in preventing the entry of pathogens, chemical pollutants, and radioactive substances into the water, soil, and air. Furthermore, ecological care promotes ecological sustainability, safeguards the ecosystem, and helps protect food and agricultural resources by preventing food waste in the hospital setting. These considerations highlight the wide-ranging positive consequences of ecological care [ 14 , 41 ].

Empirical referents

According to Walker and Avant (2011), the final step in concept analysis is to identify the empirical referents of attributes. Empirical referents do not directly serve as instruments for measuring a concept, but they provide illustrations of how defining characteristics or attributes can be recognized or measured. By presenting real-world examples, empirical referents assist in measuring the concept and validating its significance [ 21 ]. Although this study did not identify a specific independent instrument for measuring ecological care in nursing, the following examples demonstrate instruments that measure the defining characteristics or attributes of the concept.

The Nurse’s Environmental Awareness Tool (NEAT) was developed by Schenk et al. in 2015 to measure nurses’ awareness of and behaviors associated with the environmental impact of their practices. The NEAT consists of 48 two-part items in six subscales and three paired subsets as follows: nurse awareness scales, nurse professional ecological behaviors scales, and personal ecological behaviors scales [ 9 ].

The Ecological Risk Perception Scale, developed by Slimak and Dietz in 2006, examines not only the attributes of the risk itself but also the characteristics of individuals perceiving the risk. Consisting of 24 ecological risk items, the scale encompasses four subscales: ecological, chemical, global, and biological [ 50 ].

The Environmental Literacy Questionnaire (ELQ) was derived from part of Michigan State University’s project and was originally used by Kaplowitz and Levine (2005) [ 51 ]. Later, Kahyaoğlu (2011) revised the ELQ. The revised version consisted of four components: knowledge (11 items), attitude (12 items), uses (19 items), and concern (9 items) [ 52 ].

Based on the current analysis, ecological care is a multidimensional integration of thinking, attitudes, awareness, sensitivity, and literacy to deliver high-quality holistic care while maintaining environmental sustainability and promoting energy conservation.

Analysis of the concept of ecological care has significant implications for the nursing profession. Given the limited exploration of ecological care within nursing practice, conducting an analysis can empower nurses to utilize ecological factors in delivering high-quality care and embracing environmentally friendly behaviors. The objective of this study was to present a comprehensive and practical definition of ecological care, thereby establishing a shared platform for not only nurses but also other healthcare professionals to promote pro-environmental behaviors.

Backes et al. (2011) conducted a study aiming to comprehend the meaning of ecological care from the perspective of students and teachers in the healthcare field at a Public Institution of Higher Education. The study revealed several categories, including (a) ecological care as a result of relationships, interactions, and communication with the global environment (main category); (b) the development of ecological awareness (causal conditions); (c) the connection of ecological care with different systems (context); (d) the perception of human-environment-health interaction (intervention); (e) the need to foster ecological consciousness through new references (strategy); and (f) a sense of motivation to understand ecological care (result). While this study acknowledged ecological awareness and conscience as integral components of ecological care, other attributes of the concept, such as adopting an ecological perspective; ecological literacy; and the impact of values, beliefs, and organizational culture on providing holistic care, were not extensively explained [ 20 ].

The findings of a study conducted by Dahlberg et al. (2016) revealed how ecological care facilitates patients to rediscover their place in a world characterized by interconnectedness. The role of ecological care extends beyond perceiving patients within a web of relationships; it encompasses assisting patients in re-establishing their sense of self and comprehending the world anew. Ecological care entails not only combating illness but also acknowledging patients as individuals influenced by and influencing the world. Such care endeavors to facilitate rhythmic movement and create space for activity and rest, being cared for and actively participating in one’s recovery, withdrawing from the world, and re-engaging with it. This study also highlights the use of the term ecological perspective to enhance the understanding of optimal care for patients. In this study, the novel attributes of the concept of ecological care are introduced. However, the potential impacts of constructive and destructive human activities on ecosystems remain unexplored [ 1 ]. In contrast, we refer to ecological sustainability and energy conservation as significant consequences of ecological care in nursing.

In a focused ethnographic study, Al-Shamaly (2021) explored the culture of multidimensional “caring-for” practice among ICU nurses. The inclusive nature of this culture encompasses caring for oneself, patients and their families, and colleagues (including nurses and other team members) as well as ecological consciousness within the ICU environment and organization. Ecological consciousness involves caring for equipment and machines, ensuring workplace safety, reducing hazards, transitioning towards a paperless unit, maintaining thorough documentation, and demonstrating commitment and concern for the organization’s budget regarding staff and resources [ 15 ]. While this study comprehensively addresses the practical aspects of the concept, it constrains the concept of ecological care solely to ecological consciousness. However, our study revealed that ecological care is a multidimensional, and complex phenomenon that extends beyond ecological consciousness. In another study, religious values were identified as a crucial factor in promoting an ecological care orientation that can be incorporated into daily life through religious education, considering the religious and cultural context of each country. These values are instilled into individuals from childhood to adulthood through various learning activities. Therefore, religious education plays a pivotal role in shaping individuals’ commitment to ecological care [ 53 ]. According to this study, religious values significantly contribute to the development of ecological thinking and the manifestation of ecological behavior.

Moreover, a previous study by Akkuzu (2016) introduced ecological intelligence as a new type of conscience, defined as a combination of environmental awareness and the sensitivity of human beings towards adverse global alterations in nature. This understanding empowers individuals to recognize the perils faced by their communities and comprehend the underlying causes. Furthermore, it equips them with the knowledge necessary to address these perils collectively and devise effective solutions [ 54 ].

Implications for nursing practice

While our analysis primarily focused on the ecological perspective, we contend that a profound understanding of this concept is imperative for establishing cultural and political frameworks within the healthcare system. This study contributes to the limited body of research on nursing by highlighting the essentiality of ecological and holistic thinking in the domains of caregiving, treatment, management, and education. Consequently, it has the potential to yield substantial impacts in ensuring the safety of patients and healthcare providers, enhancing the quality of care, and improving patient and family satisfaction.

Limitations

The conceptual analysis is subject to several limitations. Firstly, the literature search was confined to studies published in English and Persian, potentially limiting the diversity of perspectives from other countries, cultures, and languages. To mitigate this limitation, future studies should conduct a comprehensive search in multiple languages to ensure a more holistic understanding of ecological care in nursing practice. Secondly, the analysis is susceptible to selection bias, extraction bias, and analysis bias. To address these limitations, the study selection process, data extraction, and analysis were independently conducted by two researchers. Despite these limitations, the studies were described accurately and systematically, contributing valuable insights into the concept of ecological care in nursing practice.

The results of the present analysis provide a definition of ecological care in nursing that may guide the profession to new directions of care, striving for the greater good of the patient, the profession of caring, and the environment. It is clear that more research is needed to discover the neglected importance of the environment in holistic care and to identify phenomena related to this concept in practical nursing. The literature review shows that the educational field, as the most effective factor, plays a significant role in the formation of ecological literacy and worldviews and the creation of the perceptions, attitudes, and behaviors of ecological care. In this regard, nursing professors and instructors, as the most important role models, significantly contribute to the development of the identity and character of ecological care for today’s students and future nurses.

Data availability

The data supporting the findings of this study are available upon request from the corresponding author. The data were not publicly available because of privacy or ethical restrictions.

Abbreviations

Carbon dioxide

Nitric oxide

The United Nations Educational, Scientific and Cultural Organization

Nurse’s Environmental Awareness Tool

Environmental Literacy Questionnaire

Intensive Care Unit

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Acknowledgements

This study was extracted from a research project approved and supported by the Student Research Committee, Tabriz University of Medical Sciences (grant number: 73361). The authors would like to thank all those who spent valuable time participating in this research. We are also immensely grateful to the “anonymous” reviewers for their valuable insights.

The present study was financially supported by Tabriz University of Medical Sciences, Tehran, Iran.

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Golshan Moghbeli, Mansour Ghafourifard, Shahla Shahbazi & Hanieh Aziz Karkan

Department of Nursing, Khoy University of Medical Sciences, Khoy, Iran

Amin Soheili

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GM, AS: original concept and study design; GM, HA, ShS: data collection; GM, HA, AS, MGh: data analysis and interpretation; GM, HA, AS, MGh, ShS: manuscript preparation and final critique; GM, MGh: study supervision.

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Moghbeli, G., Soheili, A., Ghafourifard, M. et al. Ecological care in nursing practice: a Walker and Avant concept analysis. BMC Nurs 23 , 614 (2024). https://doi.org/10.1186/s12912-024-02279-z

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BMC Nursing

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Measuring breastfeeding prevalence using demographic and health surveys

• Bastien Chabé-Ferret 1 , 2  

BMC Public Health volume  24 , Article number:  2366 ( 2024 ) Cite this article

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This study aims to investigate the measurement of breastfeeding prevalence indicators using Demographic and Health Surveys (DHS) data, focusing on early initiation, exclusive breastfeeding, and continued breastfeeding indicators as reported by the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF) and on the discrepancies arising from small changes in their definition.

Two hundred sixty DHS samples from 78 countries were analyzed to re-calculate usual indicators reported by WHO and UNICEF: early initiation of breastfeeding (EIB), exclusive breastfeeding under 6 months (EBF), and continued breastfeeding between 1 and 2 years (CBF12 and CBF24). Additionally, alternative estimates of the same indicators, slightly changing their definition, were calculated to test their robustness.

The WHO and UNICEF indicators for early initiation (EIB) primarily capture cases where breastfeeding is initiated “immediately” after birth, omitting those initiated within 0 or 1 hour. This discrepancy leads to substantial underestimation of levels in some regions, particularly South Asia, and in trends. Furthermore, sizable discrepancies between exclusive breastfeeding (EBF) indicators arise from the inclusion or exclusion of plain water in the definition, with significant variations across regions, especially in West and Middle Africa. However, continued breastfeeding indicators showed consistency across definitions, proving them robust for international comparisons and time trend estimations.

This study highlights the importance of understanding how breastfeeding indicators are defined and calculated using DHS data. Researchers should be cautious when using WHO and UNICEF indicators for early initiation and exclusive breastfeeding, as they may underestimate prevalence due to their narrow definition. Continued breastfeeding indicators, on the other hand, are less affected by small changes in definitions and provide reliable measures for cross-country comparisons and trend analyses. These findings underscore the need for standardized robust definitions and transparent reporting of breastfeeding indicators in global health assessments.

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Breastfeeding is consistently found to be associated with positive outcomes for mothers and children [ 1 , 2 , 3 ]. Since the late 1980s, global programs have been launched to promote early, exclusive and continued breastfeeding [ 4 , 5 , 6 , 7 ]. Accurate assessment of breastfeeding prevalence and trends is crucial to inform public health policies and interventions aimed at improving maternal and child well-being. Substantial data collection efforts allowed to monitor the evolution of various health indicators consistently in a large number of low and middle income countries. In particular, the Demographic and Health Surveys (DHS), funded by USAID, and the Multi-Indicator Cluster Surveys (MICS), funded by UNICEF, have used very similar questionnaires to measure (self-reported) breastfeeding. WHO and UNICEF routinely compile these data into country-year aggregates that are widely used by researchers and practitioners.

Among these indicators, three have been under particular scrutiny due to their alignment with the WHO recommendations in terms of breastfeeding. Indeed, the WHO recommends initiation of breastfeeding within one hour of birth, 6 months of exclusive breastfeeding, and continued breastfeeding until 24 months. To monitor progress on these recommendations, corresponding indicators have been developed based on the DHS/MICS questionnaire:

Early Initiation of Breastfeeding (EIB): The share of children born in the last 24 months who were put to the breast within one hour of birth;

Exclusive Breastfeeding under 6 months (EBF): The share of children born in the last 6 months who are currently breastfed and have not received anything else than breast milk in the last 24 hours;

Continued Breastfeeding at 12 months (CBF): The share of children aged 12 to 15 months who have received breast milk in the previous day.

These indicators, in particular for exclusive and continued breastfeeding, have been criticised for using a 24-hour recall method and pooling together children whose age differed by several months. Exclusive breastfeeding under 6 months as measured by this method has been found to provide inflated figures compared other methods asking mothers to recall whether they were still exclusively breastfeeding when the child was 6-month old [ 8 , 9 , 10 , 11 , 12 ]. Nonetheless, their alignment with WHO recommendations and their consistent availability over a broad sample of countries and years ensured their success among researchers and practitioners.

An issue that has been overlooked however is the robustness of these indicators to small changes in their definition. Indeed, some discrepancies in the coding of answers may arise across countries and waves due to differences in translation or training of enumerators. It is therefore interesting to test whether modifying slightly the definition of indicators, to accommodate differences in interpretation of the questions and answers, leads to important inconsistencies in terms of cross-country and time series comparisons.

To address this issue, data from 267 DHS surveys were re-analyzed to calculate standard breastfeeding indicators, as well as alternative ones using small modifications of their definition. These recalculations were then matched and compared to indicators issued by the WHO and UNICEF. The distribution of discrepancies over time and space between indicators was then analysed, as well as their degree of correlation.

The rest of the article starts by presenting the data, then analyzes each dimension one by one, followed by a study of the correlation between indicators, a short discussion of the implications of the results together with their limitations, and finally some concluding remarks.

In February 2020, all available standard DHS data were retrieved from the official website [ 13 ]. The objective was to re-calculate key indicators related to breastfeeding practices, including early initiation, exclusive breastfeeding, and continued breastfeeding. A total of 267 data samples were collected from 78 different countries, and the details are summarized in Table A.1. However, not all samples contained the necessary information to calculate early initiation and exclusive breastfeeding indicators, as indicated in Table  1 .

In addition, aggregate data were obtained from the UNICEF’s website [ 14 ] in both February 2020 and July 2023. Similar data were downloaded from the WHO’s website [ 15 ]. These sources were matched with the DHS data to facilitate comparisons. The matching process resulted in a somewhat reduced sample size, primarily because indicators from the initial wave of DHS surveys were not included in the WHO and UNICEF datasets. Nevertheless, the matched dataset still represented the vast majority of country-year observations, as illustrated in Figure A.1.

• Early initiation

Which indicator do the WHO/UNICEF actually report?

Indicators related to early initiation of breastfeeding are based on the question: “How long after birth did you first put the child to the breast?” Responses are categorized as “immediately,” “within x hours,” or “within y days,” with x and y values filled in by the enumerator if needed. The indicator reported by UNICEF, denoted as EIB-unicef, measures the “Percentage of children born in the last 24 months who were put to the breast within one hour of birth.” This indicator is nearly identical to the WHO’s measurement, with only minor differences due to rounding.

From the original DHS data, the three following indicators were calculated for comparison purposes:

Early Initiation of Breastfeeding (immediately) - EIB-immediate : Percentage of children born in the last 24 months who were put to breast immediately after birth;

Early Initiation of Breastfeeding (first hour) - EIB-first-hour : Percentage of children born in the last 24 months who were put to breast either immediately after birth or within 0 or 1 hour after birth;

Early Initiation of Breastfeeding (first day) - EIB-first-day : Percentage of children born in the last 24 months who were put to breast either immediately after birth, within 0 to 24 hours after birth or within 1 day after birth.

Figure  1 illustrates these indicators for each country-year pair available from the lowest to the highest prevalence of EIB-unicef (red crosses). In addition, it shows the WHO estimate (green circle) and the range going from EIB-immediate to EIB-first-hour, both re-calculated from the original DHS data (blue lines). Four main observations can be drawn from this figure:

Estimates from the WHO and UNICEF are equal, except in a handful of cases;

EIB-unicef almost always lies between EIB-immediate and EIB-first-hour;

EIB-unicef lies on average much closer to EIB-immediate than EIB-first-hour;

The difference between EIB-immediate and EIB-first-hour can become non-negligible.

Table  2 presents summary statistics, showing that UNICEF’s estimate closely aligns with the indicator EIB-immediate. Indeed, on average, EIB-unicef is much closer to EIB-immediate, with only a 4 percentage point difference, than to EIB-first-hour, standing at a 9.8 percentage point distance. This indicates that the WHO and UNICEF tend to count as “early initiation” only those cases where the answer “immediately” was reported by enumerators. They consequently overlook as “early initiation” cases where responses indicated initiation within 0 or 1 hour after birth, while they technically fall within the definition of having been put to breast within one hour of birth. This introduces an error in the measurement of early initiation, as the answer “immediately”, and its translation into different languages, may be subject to interpretations, which could vary across enumerators, countries and time. The following subsection explores whether these measurement errors bias comparisons across countries and over time.

Early initiation of breastfeeding across DHS surveys and indicators

Source: DHS surveys and WHO/UNICEF . Note: Indicators are calculated on children under 24 months

Distribution of discrepancies in indicators across time and space

The top panel of Fig.  2 displays the geographical distribution of differences between the WHO/UNICEF estimates (EIB-unicef) and the recalculated indicator (EIB-first-hour). This difference is quite tightly distributed around its mean of 9.8 percentage points, but there are some notable outliers at both ends of the distribution. Early initiation seems to be the most under-reported by WHO/UNICEF for the densely populated subregion of Southern Asia, with an average discrepancy approaching 20 pp. Conversely, the reported rates tend to lie close to re-calculated ones in Central and South America as well as the Caribbean and Southeastern Asia. Any study including those subregions in an international comparison perspective may be at risk of picking up spurious correlations.

Difference between reported and re-calculated rates of Early Initiation across time and space

Source: DHS surveys and WHO/UNICEF . Note: “pp” stands for percentage points

The bottom panel of Fig.  2 illustrates the increasing extent of misreporting over time, of approximately 4 percentage points per decade. Consequently, using WHO/UNICEF data to estimate time trends will tend to produce a downward bias. The magnitude of this downward bias is quantified in Table  3 . The table shows first the estimated slope of a linear time trend and then the average growth rate, obtained by fitting a linear time trend to the log-transformed indicators of early initiation. To obtain those averages over the whole sample, each country has been weighed by its average population over the period 1990-2019 Footnote 1 . The share of children breastfed in their first hour of life has increased by close to 1.6 percentage points per year over the last three decades in our sample, while the WHO/UNICEF estimates report an increase of only 0.95 percentage point annually, which represents a downward bias of 40% with respect to the recalculated estimate. Similarly, the average growth rate found using EIB-first-hour is 3.6% per year, while it is only 3.1% when using EIB-unicef, representing a downward bias of 14%.

• Exclusive breastfeeding

Indicators of exclusive breastfeeding are based on questions about the child’s diet, specifically: “Are you currently breastfeeding [name of the last child]?” and “At any time yesterday or last night, was [name of last child] given any of the following: Plain water? Juice? Powdered milk? Cow’s or goat’s milk? Any other liquid? (specify) Any solid or mushy food? (specify)”. The indicator reported by UNICEF, denoted as EBF-unicef, is defined as the “Percentage of infants 0-5 months of age who are fed exclusively with breast milk”, excluding plain water but allowing vitamins, medicines, and oral rehydration solution (ORS). The WHO’s indicator is virtually identical.

From the original DHS data, the two following indicators were calculated for comparison purposes:

Exclusive Breastfeeding under 6 months - EBF-strict : Percentage of children born in the last 6 months whose mother was still breastfeeding and who did not receive any food other than breast milk;

Quasi-Exclusive Breastfeeding under 6 months - EBF-quasi : Percentage of children born in the last 6 months whose mother was still breastfeeding and who did not receive any food other than breast milk or plain water.

Figure  3 displays these indicators for each country-year pair available from the lowest to the highest prevalence of EBF-unicef (red crosses). In addition, it shows the WHO estimate (green circle) and the range going from EBF-strict to EBF-quasi, both recalculated from the original DHS data (blue lines). Three main conclusions can be drawn from this figure:

Estimates from the WHO/UNICEF are extremely close to the recalculated estimate EBF-strict, except in a few cases;

Accepting plain water as part of the diet can result in substantial discrepancies.

Exclusive breastfeeding across DHS surveys and indicators

Source: DHS surveys and WHO/UNICEF . Note: Indicators are calculated on children under 6 months of age

Table  4 provides summary statistics, confirming that UNICEF’s estimate is almost identical to the re-calculated indicator EBF-strict, while the inclusion of plain water results in a substantial difference of approximately 20 percentage points. This reveals that the prevalence of children under 6 months of age being given water, while otherwise exclusively breastfed, is far from being negligible. In addition, the standard deviation of this difference is approximately 17, which suggests that there is ample variation across countries and/or over time. This introduces another source of error in the measurement of breastfeeding prevalence, as it may capture, to a large extent, differences in the risk of dehydration and/or in the awareness and reporting by parents and enumerators of ORS. The distribution of these discrepancies over time and space is documented in the next subsection.

Exclusive and quasi-exclusive breastfeeding across time and space

The top panel of Fig.  4 illustrates the geographical distribution of differences between exclusive and quasi-exclusive breastfeeding. Notably, those differences are small (less than 10 percentage points) in the American continent, while they are moderate (10-25 percentage points) in Asia to very large (over 30 percentage points) in some parts of Africa. One should therefore not use exclusive breastfeeding alone to make comparisons of breastfeeding prevalence across countries. Indeed, EBF-strict leads to the exclusion of many regions where breastfeeding is widely common, but where water is used too (even though not formula or other foods).

Difference between Exclusive and Quasi-Exclusive Breastfeeding across time and space

The bottom panel of Fig.  4 suggests that the difference between exclusive and quasi-exclusive breastfeeding has been declining slowly over time. Table  5 demonstrates that both exclusive and quasi-exclusive breastfeeding increased on average by 0.75 percentage point per year. However, exclusive breastfeeding shows a higher annual growth rate, 3%, compared to only 1.7% for quasi-exclusive breastfeeding, primarily due to the lower baseline prevalence of the former. The fast pace at which exclusive breastfeeding has increased over time partly reflects a decrease in the prevalence of water intake, rather than a take-up of breastfeeding.

Continued breastfeeding

Indicators for continued breastfeeding are based on questions about breastfeeding frequency during specific time periods (specifically, “last night between sundown and sun rise” and “yesterday during daylight”). UNICEF’s indicators, denoted as CBF12-unicef and CBF24-unicef, measure the “Percentage of children 12-15 months of age who are fed breast milk” and the “Percentage of children 12-23 months of age who were fed breast milk during the previous day,” respectively. The WHO’s indicator is virtually identical.

From the original DHS data and in particular the question: “Are you currently breastfeeding [name of the last child]?”, two indicators were calculated for comparison purposes:

Continued Breastfeeding at 12 months - CBF12-recalc : Percentage of children 12-15 months whose mother is still breastfeeding;

Continued Breastfeeding under 24 months - CBF24-recalc : Percentage of children 12-23 months whose mother is still breastfeeding.

Figure  5 displays these indicators for each country-year pair available from the lowest to the highest prevalence of continued breastfeeding breastfeeding (red crosses). In addition, it shows the WHO estimate (green circle) and the difference between UNICEF estimates and re-calculated indicators (blue lines). Tthe three following observations can be drawn:

Estimates coming from UNICEF and WHO are equal, except in a handful of cases;

Estimates from WHO/UNICEF are reasonably close to re-calculations, except in a couple of cases;

The difference between the WHO/UNICEF estimates and re-calculations tends to be larger at the lower end of the distribution.

Table  6 summarizes the statistics, indicating that UNICEF and WHO estimates closely align with the recalculated indicators CBF12-recalc and CBF24-recalc. Even though the prevalence of continued breastfeeding was calculated in a slightly different way, the differences remain negligible and are unlikely to generate spurious results. The slightly larger discrepancies at the lower end of the distribution, might reflect the greater likelihood that a mother declares that she is still breastfeeding but has actually not breastfed in the past day in a context of low prevalence of continued breastfeeding.

Continued breastfeeding across DHS surveys and indicators

Source: DHS surveys and WHO/UNICEF

Correlation between indicators

This section analyses the correlation between the different indicators. Breastfeeding is a multidimensional behavior, resulting from separate decisions at different points in time, taken under a set of information and constraints that also evolves over time. Nonetheless, one would expect that all these decisions are somewhat influenced by a general inclination towards breastfeeding, which would translate into some positive correlation between each dimension. Mothers initiating early should also tend to try and breastfeed exclusively, and breastfeed for the longest amount of time.

Table  7 reports the coefficient of correlation between all the re-calculated indicators over 217 DHS samples. A few observations are worth highlighting:

All estimates of Early Initiation correlate with each other to a relatively high degree ( \(>0.7\) );

EIB-immediate, which is the Early Initiation indicator closest to that reported by the WHO/UNICEF, is much less correlated to EBF-strict and not significantly correlated to EBF-quasi, while EIB-first-hour and EIB-first-day (Early Initiation within the first hour and within the first day, respectively) are strongly correlated to both EBF-strict and EBF-quasi;

None of the estimates of Early Initiation is significantly correlated to those of Continued Breastfeeding. EIB-immediate is even negatively correlated, even though not significantly so;

EBF-quasi is substantially more correlated to indicators of Continued Breastfeeding than EBF-strict is.

The weaker correlations of EIB-immediate and EBF-strict with other indicators of breastfeeding prevalence point to the possibility that they measure early initiation and exclusiveness with substantially more noise than EIB-first-hour and EBF-quasi do.

This analysis highlights the importance of understanding how indicators of breastfeeding prevalence are calculated using the DHS. Users should be warned that the indicator of early initiation reported by the WHO/UNICEF is most likely an underestimate, in particular for South Asia where the bias is large. The extent of this underestimation has risen over time, leading time trends to be downward biased.

In regard to indicators of exclusive breastfeeding, in a considerable number of cases, non-exclusivity is merely due to the ingestion of plain water. Accepting the ingestion of plain water to calculate an indicator of quasi-exclusive breastfeeding generates substantial discrepancies, especially for Western and Middle Africa. This does not mean that strictly exclusive breastfeeding should not be promoted, especially in places with poor access to safely drinkable water. This finding, however, highlights that low rates of exclusive breastfeeding should not necessarily be interpreted as indicating a low prevalence of breastfeeding. Indeed, low exclusive breastfeeding indicators are often interpreted as due to high reliance on formula, or early introduction of mushy foods. The fact that for a substantial part these low indicators are due to the use of plain water is relevant information.

Another question that arises is the familiarity of enumerators and respondents with Oral Rehydration Solution (ORS) [ 16 ]. Indeed, the definition of exclusive breastfeeding authorizes the ingestion of ORS, which is essentially plain water mixed with salts and sugar. However ORS does not appear in the possible modalities to answer the question: “Did [Name of last child] ingest any other liquid?” As documented in this paper, a non-negligible fraction of mothers respond “plain water” but another (smaller) fraction answers “sugar water”. One possibility is that indicators of exclusive breastfeeding are plagued by inconsistent reporting of ORS ingestion.

Indicators of continued breastfeeding are much more robust to small changes in the definition, and probably much more reliable for whom seeks to make international comparisons or estimate time trends.

A limitation to this analysis is the exclusive reliance on mothers’ self-reports, which may be subject to recall bias, social desirability or other types of bias. More objective measures are of course desirable, but hard to implement on large scale samples. This work actually tests the robustness of self-reported indicators to assess their reliability. Another limitation comes from the failure to include MICS samples, due to the numerous variations in variable names across countries and waves, which made the coding vastly more complex. Including them would allow a better representation of middle-income countries. A final limitation common to all breastfeeding measurement attempts using DHS is the total absence of high-income countries. The adoption of a similar questionnaire in existing surveys of infants for instance would be of great help to the research community.

This article documents that small changes in the definition of the indicators reported by the WHO and UNICEF can generate large discrepancies, in particular for early initiation and exclusive breastfeeding, casting doubt on international comparisons and analysis of time trends. To document these discrepancies, data coming from 260 DHS samples were re-analyzed and matched with aggregates compiled by the WHO and UNICEF. Indicators of early initiation, exclusive and continued breastfeeding, were re-calculated and found to match closely with those computed by the WHO and UNICEF. Slightly different indicators were then created for each dimension to test their robustness. In particular, the definition of early initiation was relaxed to allow for children to have been put to breast within 0 or 1 hour after birth, and quasi-exclusive breastfeeding was defined as ingestion in the previous 24 hours of breast milk or plain water.

UNICEF/WHO estimates were found to substantially underestimate both early initiation and exclusive breastfeeding, while no such bias is found for continued breastfeeding. The underestimation of early initiation is most pronounced for South Asia and has increased over time. For exclusive breastfeeding, the underestimation is particularly large for Middle and Western Africa, and has slightly decreased over time.

This work contributes to the literature on the measurement of breastfeeding prevalence [ 17 , 18 , 19 ]. It sheds light on the sensitivity of usual indicators to slight modifications of their definitions. It suggests to be cautious when interpreting cross-section comparisons and time trends and to test the robustness of the results using alternative indicators such as “initiation within one hour after birth” and “quasi-exclusive breastfeeding”. These findings may have crucial implications for the large literature on the determinants and consequences of breastfeeding prevalence [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ].

Availability of data and materials

\(\cdot\)   https://dhsprogram.com/data

\(\cdot\)   https://www.who.int/data/nutrition/nlis/data-search

\(\cdot\)   https://data.unicef.org/topic/nutrition/infant-and-young-child-feeding/

\(\cdot\)   https://data.worldbank.org/indicator/SP.POP.TOTL .

Data availability

The data for the DHS surveys is accessible from https://dhsprogram.com/data/available-datasets.cfm after registration. The data from UNICEF is accesible from https://data.unicef.org/topic/nutrition/infant-and-young-child-feeding/ . The data from the WHO is accessible from https://www.who.int/data/nutrition/nlis/data-search . A replication package containing the code to calculate all indicators used in the paper and replicate all the figures and table is available upon request.

The data was taken from the World Bank https://data.worldbank.org/indicator/SP.POP.TOTL ).

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Acknowledgements

The author thanks Mohamed Amine Izam and Benjamin Hamnache Arias for excellent research assistance. The author acknowledges financial support from from the project “Transition to Parenthood in UK SMEs”, [funder: Economic and Social Research Council (ESRC), Grant number: ES/W01002X/1], the Liser Institute for Socio Economic Research (LISER) and Middlesex University School of Business and Law.

The author acknowledges financial support from the project “Transition to Parenthood in UK SMEs”, [funder: Economic and Social Research Council (ESRC), Grant number: ES/W01002X/1], the Luxembourg Institute for Socio Economic Research (LISER) and Middlesex University School of Business and Law.

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Bastien Chabé-Ferret

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Chabé-Ferret, B. Measuring breastfeeding prevalence using demographic and health surveys. BMC Public Health 24 , 2366 (2024). https://doi.org/10.1186/s12889-024-19821-y

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Received : 07 May 2024

Accepted : 16 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1186/s12889-024-19821-y

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