Abstract
Research is a creative process and the topic of research methodology particularly realist methods is complex and varied. This chapter is designed to introduce beginners to the basics of realist research focussing on essential concepts rather than burdening the reader with voluminous detail. As realist research is a complex area, it may require you to read the chapter on a number of occasions to gain familiarity or understanding around the area. To make this easier for the reader, concepts have been illustrated with worked examples or case studies and links provided for further reading. It goes without saying that the best way of learning any new type of research methodology is to practically get involved and immerse yourself undertaking research using the methodology of interest and learning as you go along.
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References
Aston L, et al. Exploring the evidence base for how people with dementia and their informal carers manage their medication in the community: a mixed studies review. BMC Geriatr. 2017;17(1):242.
Berwick DM. The science of improvement. JAMA. 2008;299(10):1182–4.
Bunniss S, Kelly DR. Research paradigms in medical education research. Med Educ. 2010;44(4):358–66.
Carnes D, et al. The impact of a social prescribing service on patients in primary care: a mixed methods evaluation. BMC Health Serv Res. 2017;17(1):835.
Cooper C, et al. Protocol for a realist review of complex interventions to prevent adolescents from engaging in multiple risk behaviours. BMJ Open. 2017;7(9):e015477.
Dalkin SM, et al. What’s in a mechanism? Development of a key concept in realist evaluation. Implement Sci. 2015;10(1):49.
Feather JL. Developing programme theories as part of a realist evaluation of a healthcare quality improvement programme. Int J Care Coord. 2018;21(3):68–72.
Graham AC, McAleer S. An overview of realist evaluation for simulation-based education. Adv Simul. 2018;3(1):13.
Greenhalgh T, et al. Protocol—the RAMESES II study: developing guidance and reporting standards for realist evaluation. BMJ Open. 2015;5(8):e008567.
Illing J. Thinking about research: theoretical perspectives, ethics and scholarship. Understanding medical education: evidence, theory and practice. 2nd ed. Chichester: Wiley Blackwell; 2014. p. 331–48.
Jagosh J. Realist synthesis for public health: building an ontologically deep understanding of how programs work, for whom, and in which contexts. Annu Rev Public Health. 2019;40:361–72.
Jagosh J, et al. A realist evaluation of community-based participatory research: partnership synergy, trust building and related ripple effects. BMC Public Health. 2015;15:725.
Lacouture A, et al. The concept of mechanism from a realist approach: a scoping review to facilitate its operationalization in public health program evaluation. Implement Sci. 2015;10(1):153.
Langlois EV, Daniels K, Akl EA, editors. Evidence synthesis for health policy and systems: a methods guide. Geneva: World Health Organization; 2018.
Maidment I, et al. Developing a framework for a novel multi-disciplinary, multi-agency intervention (s), to improve medication management in community-dwelling older people on complex medication regimens (MEMORABLE)––a realist synthesis. Syst Rev. 2017a;6(1):125.
Maidment ID, et al. A qualitative study exploring medication management in people with dementia living in the community and the potential role of the community pharmacist. Health Expect. 2017b;20(5):929–42.
Marchal B, et al. Realist RCTs of complex interventions—an oxymoron. Soc Sci Med. 2013;94:124–8.
Papoutsi C, et al. Social and professional influences on antimicrobial prescribing for doctors-in-training: a realist review. J Antimicrob Chemother. 2017;72(9):2418–30.
Pawson R. Evidence-based policy: a realist perspective. London: Sage; 2006.
Pawson R, Tilley N. An introduction to scientific realist evaluation. Evaluation for the 21st century: a handbook; 1997. p. 405–18.
Pawson R, Tilley N. Realist evaluation. 2004. Google Scholar. 2015.
Pawson R, et al. Realist synthesis: an introduction. Manchester: ESRC Research Methods Programme, University of Manchester; 2004.
Rycroft-Malone J, et al. Realist synthesis: illustrating the method for implementation research. Implement Sci. 2012;7(1):33.
Salter KL, Kothari A. Using realist evaluation to open the black box of knowledge translation: a state-of-the-art review. Implement Sci. 2014;9(1):115.
Shearn K, et al. Building realist program theory for large complex and messy interventions. Int J Qual Methods. 2017;16(1):1609406917741796.
Van Belle S, et al. Can “realist” randomised controlled trials be genuinely realist? Trials. 2016;17(1):313.
Westhorp G. Developing complexity-consistent theory in a realist investigation. Evaluation. 2013;19(4):364–82.
Wong G. Is complexity just too complex? J Clin Epidemiol. 2013;66(11):1199–201.
Wong G, et al. Realist methods in medical education research: what are they and what can they contribute? Med Educ. 2012;46(1):89–96.
Wong G, et al. RAMESES publication standards: realist syntheses. BMC Med. 2013;11(1):21.
Wong G, et al. Interventions to improve antimicrobial prescribing of doctors in training: the IMPACT (IMProving Antimicrobial presCribing of doctors in Training) realist review. BMJ Open. 2015a;5(10):e009059.
Wong G, et al. Realist synthesis: Rameses training materials, 2013. London: The RAMESES Project; 2015b.
Acknowledgement
Thank you to Justine Tomlinson, Dr. Jon Silcock, Sarah Baig and Agostina Secchi for their helpful peer review and constructive feedback.
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Definition of Common Terms Used in Realist Research (Wong et al. 2015b)
- Realist methodology
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A theory-driven, interpretative approach to uncovering underlying middle-range theories (or logics) driving interventions and their multiple components, as well as illuminating the contextual factors that influence mechanisms of change to produce outcomes.
- Middle-range theory (MRT)
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A theory that is specific enough to generate hypotheses (e.g. in the form of propositions) to be tested in a particular case, or to help explain findings in a particular case, but general enough to apply across a number of cases or a number of domains.
- Context-mechanism-outcome (CMO) configurations
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A CMO configuration is a statement, diagram or drawing that spells out the relationship between particular features of context, particular mechanisms and particular outcomes. In a sentence, they take the form of ‘In “X” context, “Y” mechanism generates “Z” outcome’. A more detailed explanation of a CMO is as follows: ‘CMO configuring is a heuristic used to generate causative explanations pertaining to the data. The process draws out and reflects on the relationship of context, mechanism, and outcome of interest in a particular program. A CMO configuration may pertain to either the whole program or only certain aspects. One CMO may be embedded in another or configured in a series (in which the outcome of one CMO becomes the context for the next in the chain of implementation steps). Configuring CMOs is a basis for generating and/or refining the theory that becomes the final product of the review’.
- Context
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Often pertains to the ‘backdrop’ of programs and research. ‘… As these conditions change over time, the context may reflect aspects of those changes while the program is implemented. Examples of context include cultural norms and history of the community in which a program is implemented, the nature and scope of existing social networks, or built program infrastructure. … They can also be trust-building processes, geographic location effects, funding sources, opportunities, or constraints. Context can thus be broadly understood as any condition that triggers and/or modifies the behaviour of a mechanism’.
- Mechanism
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There are many definitions of mechanism. What they all have in common is that mechanisms generate outcomes. Examples include the following: ‘Mechanisms are the agents of change. They describe how the resources embedded in a program influence the reasoning and ultimately the behaviour of program subjects’. ‘…mechanisms are underlying entities, processes, or structures which operate in particular contexts to generate outcomes of interest’.There are three essential clues located in a ‘realist’ reading of mechanisms. These are that (1) mechanisms are usually hidden, (2) mechanisms are sensitive to variations in context and (3) mechanisms generate outcomes.Outcomes are not only intended outcomes (was the intervention successful or not?) but also all the intermediate outcomes as well as unplanned and/or unexpected impact of interventions. These are important because unplanned outcomes can sometimes have a greater influence on success of an intervention. Furthermore, unintended impacts may have ‘ripple effects’ in that they lead to new effects which then lead to more effects, thus changing the context of research over time.
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Zaman, H., Wong, G., Lawson, S., Maidment, I. (2020). Realist Research in Pharmacy Practice. In: Babar, ZUD. (eds) Pharmacy Practice Research Methods. Springer, Singapore. https://doi.org/10.1007/978-981-15-2993-1_6
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