Advancing scoping study methodology: a web-based survey and consultation of perceptions on terminology, definition and methodological steps
Bibliographic record
Abstract
BACKGROUND: Scoping studies (or reviews) are a method used to comprehensively map evidence across a range of study designs in an area, with the aim of informing future research practice, programs and policy. However, no universal agreement exists on terminology, definition or methodological steps. Our aim was to understand the experiences of, and considerations for conducting scoping studies from the perspective of academic and community partners. Primary objectives were to 1) describe experiences conducting scoping studies including strengths and challenges; and 2) describe perspectives on terminology, definition, and methodological steps. METHODS: We conducted a cross-sectional web-based survey with clinicians, educators, researchers, knowledge users, representatives from community-based organizations, graduate students, and policy stakeholders with experience and/or interest in conducting scoping studies to gain an understanding of experiences and perspectives on the conduct and reporting of scoping studies. We administered an electronic self-reported questionnaire comprised of 22 items related to experiences with scoping studies, strengths and challenges, opinions on terminology, and methodological steps. We analyzed questionnaire data using descriptive statistics and content analytical techniques. Survey results were discussed during a multi-stakeholder consultation to identify key considerations in the conduct and reporting of scoping studies. RESULTS: Of the 83 invitations, 54 individuals (65 %) completed the scoping questionnaire, and 48 (58 %) attended the scoping study meeting from Canada, the United Kingdom and United States. Many scoping study strengths were dually identified as challenges including breadth of scope, and iterative process. No consensus on terminology emerged, however key defining features that comprised a working definition of scoping studies included the exploratory mapping of literature in a field; iterative process, inclusion of grey literature; no quality assessment of included studies, and an optional consultation phase. We offer considerations for the conduct and reporting of scoping studies for researchers, clinicians and knowledge users engaging in this methodology. CONCLUSIONS: Lack of consensus on scoping terminology, definition and methodological steps persists. Reasons for this may be attributed to diversity of disciplines adopting this methodology for differing purposes. Further work is needed to establish guidelines on the reporting and methodological quality assessment of scoping studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.475 | 0.532 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".