P.212 Demographic Trends in Canadian Neurosurgery Training & Academic Neurosurgery
Bibliographic record
Abstract
Background: Exploring current trends in career outcomes can guide further expansion and diversity in neurosurgery demographics, as well as inform medical trainees of qualifications required for a career in neurosurgery. This study therefore aims to explore temporal trends and gender distribution in training, teaching, and leadership positions among currently practicing neurosurgeons. Methods: A list of practicing Canadian neurosurgeons and their certification year, degrees, fellowships, and teaching positions was created using publicly available information and phone/email confirmation by surgeons. Results: We identified 297 neurosurgeons currently practicing in Canada (F=32, M=265). There was a significant trend towards a greater number of neurosurgical staff having at least one advanced degree or fellowship over time (p=0.0012, p=0.0048 respectively), with no significant difference between proportions of males and females. Within academia, women represent 33% of adjunct professors, 8% of associate professors, and 15.2% of full professors. Two neurosurgical departments in Canada are led by women. Conclusions: Literature shows there is an underrepresentation of women in neurosurgery, particularly in higher-ranking teaching and leadership positions, yet our results suggest there is no significant differences in qualifications between males and females. Further exploration is needed to identify reasons underlying these trends and propose solutions to promote growth in the field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".