Gender trends in Canadian medicine and surgery: the past 30 years
Bibliographic record
Abstract
BACKGROUND: While the number of women entering medicine has steadily increased since the 1970s in Canada, the gender composition along each stage of the medical training pathway has not been comprehensively reported. We therefore sought to systematically examine the gender composition of students, residents, and practicing physicians over the past 30 years in Canada. RESULTS: In this cross-sectional analysis of Canadian medical trainees including MD applicants (137,096 male, 169,099 female), MD students (126,422 male, 152, 967 female), MD graduates (29,413 male, 34,173 female), residents by the decade (24,425 male, 28,506 female) and practicing surgeons (total 7,457 male, 3,457 female), we find that increased female representation in medicine is not matched by representation in surgery, with the key being the specialty choice process. The likelihood of female applicants matriculating to medical school was less than male applicants in the 90s (OR 0.92, 95% CI 0.92-0.93), greater in the early 2000s (OR 1.03, 95% CI 1.03-1.04), and has since balanced out (OR 1.00, 95% CI 1.00-1.01), with medical school classes being nearly 60% female for the past two decades. Despite this, females have remained underrepresented in most surgical residency programs, with odds of female medical students entering surgical residency other than Ob/Gyn being about half that of male students (OR 0.56, 95% CI 0.44-0.71), resulting in a slow increase in practicing female surgeons of less than 0.5% per year in many surgical disciplines and projected parity decades or centuries in the future. CONCLUSIONS: While undergraduate medical education has been majority female in Canada for nearly three decades, females remain greatly underrepresented in the physician workforce within surgical specialties. To build a representative medical workforce equipped to care for diverse patient populations, factors influencing the specialty choices of early career physicians will need to be examined and addressed.
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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 | 0.003 | 0.007 |
| 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".