Women in leadership positions in European neurosurgery - Have we broken the glass ceiling?
Bibliographic record
Abstract
Introduction: The proportion of male neurosurgeons has historically been higher than of women, although at least equal numbers of women have been entering European medical schools. The Diversity Committee (DC) of the European Association of Neurosurgical Societies (EANS) was founded recently to address this phenomenon. Research question: In this cross-sectional study, we aimed to characterize the status quo of female leadership by assessing the proportion of women heading European neurosurgical departments. Material and methods: European neurosurgical departments were retrieved from the EANS repository. The gender of all department chairs was determined via departmental websites or by personal contact. The proportion of females was stratified by region and by type of hospital (university versus non-university). Results: A total of 41 (4.3%) female department chairs were identified in 961 neurosurgery departments in 41 European countries. Two thirds (68.3%) of European countries do not have a female neurosurgery chair. The highest proportion of female chairs was found in Northern Europe (11.1%), owing to four female chairs in a relatively small number of departments (n = 36). The proportions were considerably smaller in Western Europe (n = 17/312 (5.5%)), Southern Europe (n = 14/353 (4.0%)) and Central and Eastern Europe (n = 6/260 (2.3%)) (p = 0.06). The distribution of female chairs in university (n = 19 (46.3%)) versus non-university departments (n = 22 (53.7%)) was even. Discussion and Conclusion: There is a significant gender imbalance with 4% of all European neurosurgery departments headed by women. The DC intends to develop strategies to support equal chances and normalize the presence of female leaders in European 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".