Bibliographic record
Abstract
BACKGROUND: In recent years, the field of medical education has sought to amplify the voices of those from traditionally marginalised groups and medical education journals have sought to become more accessible and diverse. This study sought to examine the gender and geographical representation of editors and editorial board members in medical education journals. METHODS: Information about individual editors and editorial board members of 10 medical education journals was retrieved from their websites in January 2021, including their gender and the country in which they were based. Countries were categorised according to World Bank Income Classification and World Bank Geographical Regions. We then calculated the Composite Editorial Board Diversity Score for each journal. FINDINGS: Of 488 editors and editorial board members, 283 (58.0%) were male, 452 (92.6%) were based in high-income countries and 322 (66.0%) were from the four countries with greatest representation (the United States, the United Kingdom, Australia and Canada). DISCUSSION: The composition of medical education journals' editorial leadership teams remains dominated by males and those from higher income and Western countries. Strikingly, little change has taken place since this was last examined 17 years ago despite the field becoming apparently more globalised. As medical education strives to become a more inclusive and diverse discipline, developing policies to create more globally representative editorial leadership teams should now be an urgent priority.
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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 | 0.036 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".