P.133 Women in Canadian neurosurgery: an update
Bibliographic record
Abstract
Background: Women continue to represent a minority of the neurosurgery workforce in Canada. We herein aim to provide an update of the current Canadian landscape to gain a better understanding of the factors contributing to this disparity. Methods: Chain-referral sampling, interviews, personal communications, and online resources were used as data sources. Online survey results obtained from women attending neurosurgeons across Canada were also utilized. Quantitative analyses were performed, including summary and comparative statistics. Qualitative analyses of free-text responses were performed using axial and open coding. Results: We observe a positive trend in the incoming and graduating of female residents across the country, although this trend is lagging compared to other surgical specialties. The proportion of women in active practice remains low. Positive enabling factors for success include supportive colleagues and work environment (52.6%), academic accomplishments (36.8%), and advanced fellowship training (47.4%). Perceived barriers reported included inequalities regarding career advancement opportunities (57.8%), conflicting professional and personal interests (57.8%), and lack of mentorship (36.8%). Conclusions: Women continue to represent a small proportion of practicing neurosurgeons across Canada. Our work highlights several key factors contributing to the low representation of women in neurosurgery and identifies actionable items that can be addressed by training programs and institutions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".