Women in neurointervention, a gender gap? Results of a prospective online survey
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Women's representation in medicine has increased over time yet the proportion of women practicing neurointervention remains low. We conducted an anonymous online survey through which we could explore the gender gap in neurointervention, identify potential issues, difficulties, or obstacles women might face, and evaluate if men encounter similar issues. METHODS: An online questionnaire was designed in SurveyMonkey®. Invitation to participate was emailed through national and international neurointerventional societies as well as directly through private mailing lists to men and women working in neurointervention. Responses were collected from 10 May 2019 to 10 September 2019. RESULTS: There were 295 complete responses, 173 (59%) male and 122 (41%) female. Most respondents (83%) fell within age categories 35-60 years, with representation from 40 countries across five continents. In all 95% were working full time, 73% had worked as a neurointerventionalist for >6 years, 77% worked in University-affiliated teaching institutions. Almost half of the respondents indicated no female neurointerventionalist worked in their center. Female respondents were younger and age-adjusted analysis was undertaken. Significantly fewer females than males were married and had children. Significantly fewer females held supervisory roles, held academic titles, and significantly less had a mentor. Females were less satisfied in their careers. More females felt they receive less recognition than colleagues of the opposite sex. Males had a greater proportion of work time dedicated to neurointervention. Similar proportions of both genders experienced bullying in work (40%-47%); however, sexual harassment was more common for females. There were no differences between genders in how they dealt with complications or their effects on mental well-being. CONCLUSION: There are many potential reasons why women are underrepresented in neurointervention, however, the literature suggests this is not unique to our specialty. Multiple long-term strategies will be necessary to address these issues, some of which are discussed in the article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".