Best practice guidance and reporting items for the development of scoping review protocols
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to clearly describe how to develop a robust and detailed scoping review protocol, which is the first stage of the scoping review process. This paper provides detailed guidance and a checklist for prospective authors to ensure that their protocols adequately inform both the conduct of the ensuing review and their readership. INTRODUCTION: Scoping reviews are a common approach to evidence synthesis for researchers, clinicians, and policymakers across a variety of fields. Scoping reviews are not concerned with making analytical comparisons based on pooling results data from multiple primary sources of evidence, but rather on collating and describing the evidence and presenting the summation in a clearly illustrated format. Methods for undertaking and reporting scoping reviews continue to be refined. Some prospective reviewers may be uncertain how to plan, structure, and report scoping review protocols, as there is little or no specific guidance for scoping review protocols yet available. METHODS: This guidance was developed by members of the JBI Scoping Review Methodology Group based on previous experience and expertise in developing scoping review and evidence synthesis methodologies, protocols, and reviews, as well as through experiences working with and guiding authors to develop scoping review protocols. Elements of a comprehensive scoping review protocol are outlined and explained in detail. CONCLUSION: Knowledge users of evidence syntheses rely on clear and transparent reporting to understand and use the results of published work to drive evidence-based improvements within health care and beyond. It is hoped that readers will be able to use this guidance when developing protocols to assist them in planning future scoping reviews and to carry them out with a high degree of transparency.
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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 | 0.522 | 0.768 |
| Meta-epidemiology (narrow) | 0.007 | 0.013 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.034 | 0.046 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.015 | 0.022 |
| Research integrity | 0.028 | 0.035 |
| Insufficient payload (model declined to judge) | 0.096 | 0.098 |
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".