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Systematic searching for theory to inform systematic reviews: is it feasible? Is it desirable?
Abstract
Background: In recognising the potential value of theory in understanding how interventions work comes a challenge – how to make identification of theory less haphazard? Objectives: To explore the feasibility of systematic identification of theory. Method: We searched PubMed for published reviews (1998–2012) that had explicitly sought to identify theory. Systematic searching may be characterised by a structured question, methodological filters and an itemised search procedure. We constructed a template (BeHEMoTh – Behaviour of interest; Health context; Exclusions; Models or Theories) for use when systematically identifying theory. The authors tested the template within two systematic reviews. Results: Of 34 systematic reviews, only 12 reviews (35%) reported a method for identifying theory. Nineteen did not specify how they identified studies containing theory. Data were unavailable for three reviews. Candidate terms include concept(s)/conceptual, framework(s), model(s), and theory/theories/theoretical. Information professionals must overcome inadequate reporting and the use of theory out of context. The review team faces an additional concern in lack of ‘theory fidelity’. Conclusions: Based on experience with two systematic reviews, the BeHEMoTh template and procedure offers a feasible and useful approach for identification of theory. Applications include realist synthesis, framework synthesis or review of complex interventions. The procedure requires rigorous evaluation.
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“To develop a more systematic approach to searching this subject area, we employed a new, iterative approach called the BeHEMoTh (Behavior of interest; Health context; Exclusions; Models or Theories) framework [24] to inform our search protocol and to build a list of names of KT theories, models and frameworks.”
From Scoping review identifies significant number of knowledge translation theories, models, and frameworks with limited use (2018) · cites this paper as [24]
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Self-citationKhany, R., & Kamalvand, A. (2022) · Teaching English as a Second or Foreign Language--TESL-EJ · doi:10.55593/ej.25100a2 · Cited by 8
Chambers, D., Cantrell, A. J., & Booth, A. (2020) · Health Services and Delivery Research · doi:10.3310/hsdr08020 · Cited by 9
Self-citationNikonorova, E. V., & Shibaeva, E. А. (2020) · Bibliotekovedenie [Russian Journal of Library Science] · doi:10.25281/0869-608x-2020-69-2-119-134 · Cited by 7
Evans, C., Clancy, G., Evans, K., Booth, A., Nazmeen, B., Sunney, C., Clowes, M., Jones, N. W., Timmons, S., & Spiby, H. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-079153 · Cited by 8
Self-citation
Metadata
- Authors
- Booth, A., & Carroll, C.
- Affiliations
- University of Sheffield (GB)
- Corresponding author
- Booth, Andrew
- Venue
- Health Information & Libraries Journal
- Publisher
- Wiley
- Published
- 2015-06-11
- Volume
- 32
- Issue
- 3
- Pages
- 220-235
- ISSN
- 1471-1834 (ISSN-L)
- 1471-1842
- OpenAlex
- W1935977214
- Cited by
- 196
- References
- 52
- Citations by year
- 2026: 10 · 2025: 26 · 2024: 33 · 2023: 27 · 2022: 17 · 2021: 26 · 2020: 19 · 2019: 10 · 2018: 11 · 2017: 10 · 2016: 6 · 2015: 1
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Health Information & Libraries Journal (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Health Sciences › Nursing › Research and Theory › Methodologies in Health Research and Practice
- Keywords
- systematic reviews, search strategy design, realist synthesis, framework synthesis, complex interventions, reporting quality
- MeSH terms
- Abstracting and Indexing, Humans, Review Literature as Topic, Databases, Bibliographic, Information Storage and Retrieval
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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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