Examining the WFME Recognition Programme at 10 years
Bibliographic record
Abstract
BACKGROUND: In 2012, the World Federation for Medical Education (WFME) evaluated and formally recognized the first agency in its Recognition Programme (RP). The RP was developed to review accrediting authorities in response to a 2010 policy by the Educational Commission for Foreign Medical Graduates (ECFMG) to require international medical graduates (IMGs) seeking to practice in the U.S. to graduate from an appropriately accredited medical school. By the end of 2022, WFME had recognized 33 accrediting bodies and received applications from another 16, which accounted for over three-quarters of the world's medical schools. In 2023, WFME leadership changed hands, and the ECFMG will take its first steps toward implementing its Recognized Accreditation Policy. APPROACH: In this article, we look back at the genesis of the RP and describe its first decade as informed by the limited existing peer-reviewed literature and the emerging activities of accrediting agencies that could have significant implications for the quality of medical education internationally. CONCLUSIONS: The rapidly growing influence of WFME on medical education worldwide has largely occurred without significant awareness or scrutiny, and there is a need for the WFME to demonstrate greater transparency, proactively engage its stakeholders, and support research and evaluation.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".