P.113 Mind the gap: illuminating gender disparities in neurosurgical inclusion and diversity
Bibliographic record
Abstract
Background: Gender disparities endure in neurosurgery, impacting the experiences of female practitioners. Unveiling these challenges is crucial for promoting inclusivity and addressing the unique obstacles faced by women in the field. Methods: An international survey designed using a physician wellness framework was sent to neurosurgeons between June 2021 and November 2021. Univariate analysis (Kruskal-Wallis Test) was performed to assess feelings of inclusion and diversity as a function of gender. Results: Of the total 384 respondents (65% neurosurgeons, 6% fellows, and 29% residents), 71% identified as male, 27% as female, and 2% as other. Compared to their male colleagues, female neurosurgeons more strongly endorsed feeling that their career progression has been limited by their gender (p<0.05) and were less likely to feel entrusted in their surgical ability (p<0.05) or to have equal access to surgical resources (p<0.05). Furthermore, they were less likely to endorse feelings that leaders in their department were committed to creating an inclusive environment (p<0.05). Conclusions: Our survey sheds light on significant gender-related disparities in neurosurgery. Female neurosurgeons express heightened concerns about gender-limiting career progression, reduced trust in their surgical abilities, and disparities in resource access. These findings underscore the imperative to foster a more inclusive and supportive environment within 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".