Cross-sectional analysis of women in neurosurgery: a Canadian perspective
Bibliographic record
Abstract
OBJECTIVE: Although the past decades have seen a steady increase of women in medicine in general, women continue to represent a minority of the physician-training staff and workforce in neurosurgery in Canada and worldwide. As such, the aim of this study was to analyze the experiences of women faculty practicing neurosurgery across Canada to better understand and address the factors contributing to this disparity. METHODS: A historical, cross-sectional, and mixed-method analysis of survey responses was performed using survey results obtained from women attending neurosurgeons across Canada. A web-based survey platform was utilized to collect responses. Quantitative analyses were performed on the responses from the study questionnaire, including summary and comparative statistics. Qualitative analyses of free-text responses were performed using axial and open coding. RESULTS: A total of 19 of 31 respondents (61.3%) completed the survey. Positive enabling factors for career success included supportive colleagues and work environment (52.6%); academic accomplishments, including publications and advanced degrees (36.8%); and advanced fellowship training (47.4%). Perceived barriers reported included inequalities with regard to career advancement opportunities (57.8%), conflicting professional and personal interests (57.8%), and lack of mentorship (36.8%). Quantitative analyses demonstrated emerging themes of an increased need for women mentors as well as support and recognition of the contributions to career advancement of personal and family-related factors. CONCLUSIONS: This study represents, to the authors' knowledge, the first analysis of factors influencing career success and satisfaction in women neurosurgeons across Canada. This study highlights several key factors contributing to the low representation of women in neurosurgery and identifies specific actionable items that can be addressed by training programs and institutions. In particular, female mentorship, opportunities for career advancement, and increased recognition and integration of personal and professional roles were highlighted as areas for future intervention. These findings will provide a framework for addressing these factors and improving the recruitment and retention of females in this specialty.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".