Gender imbalance amongst promotion and leadership in academic surgical programs in Canada: A cross-sectional Investigation
Bibliographic record
Abstract
BACKGROUND: Women are underrepresented at higher levels of promotion or leadership despite the increasing number of women physicians. In surgery, this has been compounded by historical underrepresentation. With a nation-wide focus on the importance of diversity, our aim was to provide a current snapshot of gender representation in Canadian universities. METHODS: This cross-sectional online website review assessed the current faculty listings for 17 university-affiliated academic surgical training departments across Canada in the 2019/2020 academic year. Gender diversity of academic surgical faculty was assessed across surgical disciplines. Additionally, gender diversity in career advancement, as described by published leadership roles, promotion and faculty appointment, was analyzed. RESULTS: Women surgeons are underrepresented across Canadian surgical specialties (totals: 2,689 men versus 531 women). There are significant differences in the gender representation of surgeons between specialties and between universities, regardless of specialty. Women surgeons had a much lower likelihood of being at the highest levels of promotion (OR: 0.269, 95% CI: 0.179-0.405). Men surgeons were statistically more likely to hold academic leadership positions than women (p = 0.0002). Women surgeons had a much lower likelihood of being at the highest levels of leadership (OR: 0.372, 95% CI: 0.216-0.641). DISCUSSION: This study demonstrates that women surgeons are significantly underrepresented at the highest levels of academic promotion and leadership in Canada. Our findings allow for a direct comparison between Canadian surgical subspecialties and universities. Individual institutions can use these data to critically appraise diversity policies already in place, assess their workforce and apply a metric from which change can be measured.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".