P.112 Understanding obstacles: a neurosurgical view on gender disparities in career progression
Bibliographic record
Abstract
Background: Gender disparities persist in neurosurgery, unfortunately impacting career progression for women. Understanding these challenges is vital for fostering inclusivity. 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 various aspects of perceived career progression as a function of gender. Results: Of the total 537 respondents (64% neurosurgeons, 6% fellows, and 30% residents), 69% identified as male, 29% as female, and 2% as other. Compared to their male colleagues, female neurosurgeons expressed greater desire to advance in their career (p<0.05) and to leave their home country in the interest of job prospects (p<0.05). Despite these aspirations, female neurosurgeons reported that they did not have available career advancement opportunities (p<0.05), that the culture in their country inhibited their career advancement (p<0.05), and that they felt subject to harassment at their workplace (p<0.05). Conclusions: Our survey highlights significant gender-related obstacles in neurosurgical career advancement. Female neurosurgeons express strong career aspirations but face barriers such as limited opportunities, cultural impediments, and harassment. Addressing these challenges is crucial for achieving gender equity in neurosurgery.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".