Examining the readiness of best evidence in medical education guides for integration into educational practice: A meta-synthesis
Bibliographic record
Abstract
BACKGROUND: To support evidence-informed education, health professions education (HPE) stakeholders encourage the creation and use of knowledge syntheses or reviews. However, it is unclear if these knowledge syntheses are ready for translation into educational practice. Without understanding the readiness, defined by three criteria-quality, accessibility and relevance-we risk translating weak evidence into practice and/or providing information that is not useful to educators. METHODS: A librarian searched Web of Science for knowledge syntheses, specifically Best Evidence in Medical Education (BEME) Guides. This meta-synthesis focuses on BEME Guides because of their explicit goal to inform educational practice and policy. Two authors extracted data from all Guides, guided by the 25-item STructured apprOach to the Reporting In healthcare education of Evidence Synthesis (STORIES). RESULTS: Forty-two Guides published in Medical Teacher between 1999 and 2017 were analyzed. No Guide met all STORIES criteria, but all included structured summaries and most described their literature search (n = 39) and study inclusion/exclusion (n = 40) procedures. Eleven Guides reported the presence of theory and/or educational principles, and eight consulted with external subject matter experts. Accessibility to each Guide's full-text and supplemental materials was variable. DISCUSSION: For a subset of HPE knowledge syntheses, BEME Guides, this meta-synthesis identifies factors that support readiness and indicates potential areas of improvement, such as consistent access to Guides and inclusion of external subject matter experts on the review team. This analysis is useful for understanding the current readiness of HPE knowledge syntheses and informing future reviews to evolve so they can catalyze translation of evidence into educational practice.
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.
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 | 0.196 | 0.559 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".