Describing the Evidence Base for Accreditation in Undergraduate Medical Education Internationally: A Scoping Review
Bibliographic record
Abstract
PURPOSE: To summarize the state of evidence related to undergraduate medical education (UME) accreditation internationally, describe from whom and where the evidence has come, and identify opportunities for further investigation. METHOD: The authors searched Embase, ERIC, PubMed, and Scopus from inception through January 31, 2018, without language restrictions, to identify peer-reviewed articles on UME accreditation. Articles were classified as scholarship if all Glassick's criteria were met and as nonscholarship if not all were met. Author, accrediting agency, and study characteristics were analyzed. RESULTS: Database searching identified 1,379 nonduplicate citations, resulting in 203 unique, accessible articles for full-text review. Of these and with articles from hand searching added, 36 articles were classified as scholarship (30 as research) and 85 as nonscholarship. Of the 36 scholarship and 85 nonscholarship articles, respectively, 21 (58%) and 44 (52%) had an author from the United States or Canada, 8 (22%) and 11 (13%) had an author from a low- or middle-income country, and 16 (44%) and 43 (51%) had an author affiliated with a regulatory authority. Agencies from high-income countries were featured most often (scholarship: 28/60 [47%]; nonscholarship: 70/101 [69%]). Six (17%) scholarship articles reported receiving funding. All 30 research studies were cross-sectional or retrospective, 12 (40%) reported only analysis of accreditation documents, and 5 (17%) attempted to link accreditation with educational outcomes. CONCLUSIONS: Limited evidence exists to support current UME accreditation practices or guide accreditation system creation or enhancement. More research is required to optimize UME accreditation systems' value for students, programs, and society.
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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 | 0.071 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.041 | 0.036 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".