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A worked example of “best fit” framework synthesis: a systematic review of views concerning the taking of some potential chemopreventive agents
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BACKGROUND: A variety of different approaches to the synthesis of qualitative data are advocated in the literature. The aim of this paper is to describe the application of a pragmatic method of qualitative evidence synthesis and the lessons learned from adopting this "best fit" framework synthesis approach. METHODS: An evaluation of framework synthesis as an approach to the qualitative systematic review of evidence exploring the views of adults to the taking of potential agents within the context of the primary prevention of colorectal cancer. RESULTS: Twenty papers from North America, Australia, the UK and Europe met the criteria for inclusion. Fourteen themes were identified a priori from a related, existing conceptual model identified in the literature, which were then used to code the extracted data. Further analysis resulted in the generation of a more sophisticated model with additional themes. The synthesis required a combination of secondary framework and thematic analysis approaches and was conducted within a health technology assessment timeframe. CONCLUSION: The novel and pragmatic "best fit" approach to framework synthesis developed and described here was found to be fit for purpose. Future research should seek to test further this approach to qualitative data synthesis.
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![Figure 1 A priori themes reflecting people's views about taking potential chemopreventive agents, derived from Huffman 2002[16].](/paper-figures/cb868fbc7ae244a89b786cfda62b5cde/a-worked-example-of-best-fit-framework-synthesis-a-systematic-review-of-views-figure-1.jpg)

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“At the third level, framework synthesis (Carroll et al., 2011) was applied to map the synthesized evidence onto the fourdomain conceptual architecture, epistemological, pedagogical, sociotechnical, and civic-democratic, developed in the conceptual background of the review.”
From Between cognitive offloading and critical autonomy: A systematic review of the epistemic implications of generative AI in higher education. (2026) · cites this paper as Carroll et al., 2011
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A methodological quest for systematic literature mapping
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Self-citation
Metadata
- Authors
- Carroll, C., Booth, A., & Cooper, K.
- Affiliations
- University of Sheffield (GB)
- Corresponding author
- Carroll, Christopher
- Venue
- BMC Medical Research Methodology
- Published
- 2011-03-16
- Volume
- 11
- Issue
- 1
- Pages
- 29-29
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC3068987
- OpenAlex
- W2068075745
- Cited by
- 461
- References
- 34
- Citations by year
- 2026: 53 · 2025: 31 · 2024: 51 · 2023: 54 · 2022: 35 · 2021: 58 · 2020: 40 · 2019: 21 · 2018: 27 · 2017: 20 · 2016: 22 · 2015: 12 · 2014: 6 · 2013: 15 · 2012: 14
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Research Methodology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Education › Qualitative Research Methods and Applications
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Keywords
- qualitative evidence synthesis, chemopreventive agents, patient perspectives, conceptual model, thematic analysis, health technology assessment
- MeSH terms
- Adult, Evaluation Studies as Topic, Humans, Models, Psychological, Research Design, Colorectal Neoplasms, Anticarcinogenic Agents, Chemoprevention
About these checks
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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