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Realist synthesis: illustrating the method for implementation research
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BACKGROUND: Realist synthesis is an increasingly popular approach to the review and synthesis of evidence, which focuses on understanding the mechanisms by which an intervention works (or not). There are few published examples of realist synthesis. This paper therefore fills a gap by describing, in detail, the process used for a realist review and synthesis to answer the question 'what interventions and strategies are effective in enabling evidence-informed healthcare?' The strengths and challenges of conducting realist review are also considered. METHODS: The realist approach involves identifying underlying causal mechanisms and exploring how they work under what conditions. The stages of this review included: defining the scope of the review (concept mining and framework formulation); searching for and scrutinising the evidence; extracting and synthesising the evidence; and developing the narrative, including hypotheses. RESULTS: Based on key terms and concepts related to various interventions to promote evidence-informed healthcare, we developed an outcome-focused theoretical framework. Questions were tailored for each of four theory/intervention areas within the theoretical framework and were used to guide development of a review and data extraction process. The search for literature within our first theory area, change agency, was executed and the screening procedure resulted in inclusion of 52 papers. Using the questions relevant to this theory area, data were extracted by one reviewer and validated by a second reviewer. Synthesis involved organisation of extracted data into evidence tables, theming and formulation of chains of inference, linking between the chains of inference, and hypothesis formulation. The narrative was developed around the hypotheses generated within the change agency theory area. CONCLUSIONS: Realist synthesis lends itself to the review of complex interventions because it accounts for context as well as outcomes in the process of systematically and transparently synthesising relevant literature. While realist synthesis demands flexible thinking and the ability to deal with complexity, the rewards include the potential for more pragmatic conclusions than alternative approaches to systematic reviewing. A separate publication will report the findings of the review.
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“Here, it is important to understand that scoping reviews are not the same as ‘realist reviews’ which can also be used to examine how interventions or programmes work.”
From Scoping reviews: reinforcing and advancing the methodology and application (2021) · cites this paper as [33]
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“Realist reviews are typically designed to ellucide the theories that underpin a programme, examine evidence to reveal if and how those theories are relevant and explain how the given programme works (or not) [33].”
From Scoping reviews: reinforcing and advancing the methodology and application (2021) · cites this paper as [33]
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Williams, N. H., Hawkes, C., Din, N. U., Roberts, J. L., Charles, J. M., Morrison, V. L., Hoare, Z., Edwards, R. T., Andrew, J. G., Alexander, S., et al. (2015) · Pilot and Feasibility Studies · doi:10.1186/s40814-015-0008-0 · Cited by 14
Poltawski, L., Abraham, C., Förster, A., Goodwin, V. A., Kilbride, C., Taylor, R. S., & Dean, S. G. (2013) · Implementation Science · doi:10.1186/1748-5908-8-115 · Cited by 14
Terminology for Dissemination and Implementation Research
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de Jong, M. A. J. G., Wagemakers, A., & Koelen, M. A. (2019) · BMC Public Health · doi:10.1186/s12889-019-6389-x · Cited by 14
Thompson, N., Odell‐Miller, H., Underwood, B. R., Wolverson, E., & Hsu, M. (2024) · Nature Mental Health · doi:10.1038/s44220-024-00342-x · Cited by 15
Treacy, M., Wong, G., Odell, M., & Roberts, N. W. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-062154 · Cited by 17
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Self-citation
Metadata
- Authors
- Rycroft-Malone, J., McCormack, B., Hutchinson, A. M., DeCorby, K., Bucknall, T. K., Kent, B., Schultz, A., Snelgrove-Clarke, E., Stetler, C. B., Titler, M., Wallin, L., & Wilson, V.
- Affiliations
- University of Ulster (GB)
- Deakin University (AU)
- McMaster University (CA)
- Boston University (US)
- University of Michigan (US)
- Karolinska Institutet (SE)
- Venue
- Implementation Science
- Published
- 2012-04-19
- Volume
- 7
- Issue
- 1
- Pages
- 33-33
- ISSN
- 1748-5908 (ISSN-L)
- PMC
- PMC3514310
- OpenAlex
- W2097730860
- Cited by
- 742
- References
- 32
- Citations by year
- 2026: 54 · 2025: 71 · 2024: 92 · 2023: 66 · 2022: 84 · 2021: 82 · 2020: 55 · 2019: 46 · 2018: 48 · 2017: 49 · 2016: 29 · 2015: 27 · 2014: 19 · 2013: 14 · 2012: 1
- FWCI
- 34.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Implementation Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Queen Margaret University Publications Repository (Queen Margaret University) (acceptedVersion)
- PEARL (University of Plymouth) (submittedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Queen Margaret University Publications Repository (Queen Margaret University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Keywords
- realist synthesis, realist review, causal mechanisms, change agency, implementation research, complex interventions, theoretical framework, data extraction, hypothesis generation
- MeSH terms
- Health Services Research, Humans, Review Literature as Topic, Evidence-Based Medicine
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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