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Record W4388755876 · doi:10.1080/0142159x.2023.2279908

Examining the WFME Recognition Programme at 10 years

2023· article· en· W4388755876 on OpenAlexaff
Sean Tackett, Cynthia Whitehead, Ahmed Rashid

Bibliographic record

VenueMedical Teacher · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWomen's College HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAccreditationScrutinyMedical educationCommissionAgency (philosophy)Political scienceTransparency (behavior)MedicinePublic relationsSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0210.005

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.

Opus teacher head0.119
GPT teacher head0.363
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2023
Admission routes1
Has abstractyes

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