Knowledge Syntheses in Medical Education: Demystifying Scoping Reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it