Assessing gender and racial disparities in medical education leadership: the role of academic credentials
Bibliographic record
Abstract
Introduction Despite growing attention to diversity in academic medicine, gender and racial disparities persist in medical school leadership. This study examined how advanced academic qualifications, such as graduate degrees and additional certifications, intersect with these disparities in Canadian medical school leadership positions. Methods We conducted a cross-sectional analysis across 17 accredited Canadian medical schools, categorising faculty by qualifications, medical school leadership roles and academic rank. Data sources included institutional faculty directories, LinkedIn and Scopus. Race and gender were inferred using NamSor. We used the χ 2 tests and effect size reporting for analyses. Results Across qualification levels, gender and racial disparities in leadership and academic rank remained evident. Men and White faculty were disproportionately represented in senior roles, particularly among MDs who also held additional graduate degrees such as a master’s or PhD, where disparities were most pronounced. In contrast, women and racialised faculty were more frequently found in mid-level or junior roles, even when holding multiple advanced degrees. These findings indicate that additional credentials alone do not mitigate inequities in academic advancement. Conclusion Our findings suggest that while advanced qualifications may enhance access to leadership roles, they do not close gender and racial gaps. These persistent disparities highlight the need for systemic reforms and targeted policies to ensure equitable leadership opportunities in academic medicine.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".