Gender representation in Canadian surgical leadership and medical faculties: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Over the past two and half decades, Canadian medical school students have become majority female, and the medical workforce is therefore increasingly comprised of female physicians. Whether this change, however, has been reflected in the gender balance within medical school faculty positions and leadership has not been well studied in Canada. METHODS: This cross-sectional study examined the genders of full-time faculty members from the most recently available AFMC data, the current heads of departments of medicine and surgery from department websites and confirmed with respective universities. RESULTS: Overall, women held 40.5% of full-time faculty positions in Canadian faculties of medicine. Female representation decreased with increasing academic rank, from 57.8% of instructors to 50.8% of assistant, 39.2% of associate, and 28.1% of full professors, respectively, with the greatest rate of increase over the past decade among full professors (0.75% per year). The heads of departments of family medicine were majority female (67%), and heads internal medicine at parity (50% female), consistent with numbers of practicing physicians. However, the heads of surgical divisions were majority male (86% overall). Accounting for the gender balance of practicing surgeons, male compared to female surgeons were 2.9 times as likely to be division head (95% CI 1.78-4.85, p < 0.0001). CONCLUSIONS: Women remain underrepresented in Canadian faculties of medicine in leadership positions. Leadership in departments of surgery has particularly low female representation, even relative to the proportion of practicing female surgeons within the respective discipline.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".