Gender disparity in academic orthopedic programs in Canada: a cross-sectional study
Bibliographic record
Abstract
<h3>Background:</h3> The majority of the literature on gender disparity in orthopedic surgery is from the United States; the Canadian perspective is lacking. The objective of this study was to determine the representation of women faculty members and the proportion of women faculty in published leadership positions in academic orthopedic divisions and departments across Canada. <h3>Methods:</h3> In this cross-sectional study, we used a Web-based search strategy to identify faculty listings for all 17 academic orthopedic programs affiliated with the Association of Faculties of Medicine of Canada for the 2018/19 academic year. For each faculty member identified, we determined gender (man or woman), professorial rank and leadership positions. We compared regional gender differences among 3 groups: schools in eastern Canada and Quebec (6), Ontario (6) and western Canada (5). Gender comparisons were made for all variables of interest. <h3>Results:</h3> We identified 809 orthopedic surgeons at the 17 Canadian academic institutions, of whom 96 (11.9%) were women. In eastern Canada and Quebec, 16.2% of the faculty were women, significantly above the national average (<i>p</i> = 0.03). The corresponding values for Ontario and western Canada were 8.9% (<i>p</i> = 0.1) and 11.4% (<i>p</i> = 0.7). There were no significant differences in the proportions of women and men at lower levels of promotion, but significantly more men than women had attained full professorship (65 [9%] v. 1 [1%], <i>p</i> = 0.002). Women surgeons were not represented in leadership roles or within faculty roles of distinction. <h3>Conclusion:</h3> In 2018/19, women orthopedic surgeons were underrepresented in faculty positions across academic orthopedic training programs in Canada, and were disproportionately underrepresented in promoted academic faculty roles and leadership positions. These data can be used to review and educate on equity in hiring and promotion, as well as to foster mentorship and transition planning.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".