Knowledge Syntheses in Medical Education: Demystifying Scoping Reviews
Bibliographic record
Abstract
An unprecedented rise in health professions education (HPE) research has led to increasing attention and interest in knowledge syntheses. There are many different types of knowledge syntheses in common use, including systematic reviews, meta-ethnography, rapid reviews, narrative reviews, and realist reviews. In this Perspective, the authors examine the nature, purpose, value, and appropriate use of one particular method: scoping reviews. Scoping reviews are iterative and flexible and can serve multiple main purposes: to examine the extent, range, and nature of research activity in a given field; to determine the value and appropriateness of undertaking a full systematic review; to summarize and disseminate research findings; and to identify research gaps in the existing literature. Despite the advantages of this methodology, there are concerns that it is a less rigorous and defensible means to synthesize HPE literature. Drawing from published research and from their collective experience with this methodology, the authors present a brief description of scoping reviews, explore the advantages and disadvantages of scoping reviews in the context of HPE, and offer lessons learned and suggestions for colleagues who are considering conducting scoping reviews. Examples of published scoping reviews are provided to illustrate the steps involved in the methodology.
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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 | 0.692 | 0.835 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.081 | 0.054 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.037 | 0.034 |
| Open science | 0.011 | 0.027 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".