An Evidence‐Based Approach to Scoping Reviews
Bibliographic record
Abstract
OBJECTIVE: Scoping reviews are used to assess the extent of a body of literature on a particular topic, and often to ensure that further research in that area is a beneficial addition to world knowledge. The aim of this paper reports upon the development of a methodology for scoping reviews based upon the Arksey and O'Malley framework, the Levac, Colquhoun, and O'Brien, and the Joanna Briggs Institute methods of evidence synthesis. METHODS: A working group consisting of members of the Joanna Briggs collaborating organizations met to discuss the proposed framework for the methodology and develop a draft for the scoping review methodology based on the Arksey and O'Malley framework and the work of Levac et al. This was followed by a workshop attended by other members of the organizations consisting of 30 international researchers to discuss the proposed methodology. Further refinement of the methodology was undertaken as a result of the feedback received from the workshop. RESULTS: The development of the methodology focused on five stages of the protocol and review development. These were identifying the research question by clarifying and linking the purpose and research question, identifying the relevant studies using a three-step literature search in order to balance feasibility with breadth and comprehensiveness, careful selection of the studies to using a team approach, charting the data and collating the results to identify the implications of the study findings for policy, practice, or research. LINKING EVIDENCE TO ACTION: The current methodology recommends including both quantitative and qualitative research, as well as evidence from economic and expert opinion sources to answer questions of effectiveness, appropriateness, meaningfulness and feasibility of health practices and delivery methods. The proposed framework has the potential to provide options when faced with complex concepts or broad research questions.
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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 | 0.618 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.080 | 0.066 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.037 | 0.019 |
| Open science | 0.017 | 0.029 |
| Research integrity | 0.022 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".