The voices of medical education scholarship: Describing the published landscape
Bibliographic record
Abstract
INTRODUCTION: The voices of authors who publish medical education literature have a powerful impact on the field's discourses. Researchers have identified a lack of author diversity, which suggests potential epistemic injustice. This study investigates author characteristics to provide an evidence-based starting point for communal discussion with the intent to move medical education towards a future that holds space for, and values, diverse ways of knowing. METHOD: The authors conducted a bibliometric analysis of all articles published in 24 medical education journals published between 2000 and 2020 to identify author characteristics, with an emphasis on author gender and geographic location and their intersection. Article metadata was downloaded from Web of Science. Genderize.io was used to predict author gender. RESULTS: The journals published 37 263 articles authored by 62 708 unique authors. Males were more prevalent across all authorship positions (n = 62 828; 55.7%) than females (n = 49 975; 44.3%). Authors listed affiliations in 146 countries of which 95 were classified as Global South. Few articles were written by multinational teams (n = 3765; 16.2%). Global South authors accounted for 12 007 (11.4%) author positions of which 3594 (3.8%) were female. DISCUSSION: This study provides an evidence-based starting point to discuss the imbalance of author voices in medical education, especially when considering the intersection of gender and geographical location, which further suggests epistemic injustice in medical education. If the field values a diversity of perspectives, there is considerable opportunity for improvement by engaging the community in discussions about what knowledge matters in medical education, the role of journals in promoting diversity, how to best use this baseline data and how to continue studying epistemic injustice in medical education.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.035 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.038 | 0.026 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".