Reconsidering a Global Agency for Medical Education: Back to the Drawing Board?
Bibliographic record
Abstract
Issue: The World Federation for Medical Education (WFME) was established in 1972 and in the five decades that followed, has been the de facto global agency for medical education. Despite this apparently formidable remit, it has received little analysis in the academic literature. Evidence: In this article, we examine the historical context at the time WFME was established and summarize the key decisions it has taken in its history to date, highlighting particularly how it has adopted positions and programmes that have seemingly given precedence to the values and priorities of countries in the Global North. In doing so, we challenge the inevitability of the path that it has taken and consider other possible avenues that such a global agency in medical education could have taken, including to advocate for, and to develop policies that would support countries in the Global South. Implications: This article proposes a more democratic and equitable means by which a global organization for medical education might choose its priority areas, and a more inclusive method by which it could engage the medical education community worldwide. It concludes by hypothesizing about the future of global representation and priority-setting, and outlines a series of principles that could form the basis for a reimagined agency that would have the potential to become a force for empowerment and global justice in medical education.
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 | 0.044 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.021 | 0.040 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".