Abstract TMP75: Gender Disparities in International Stroke Conference Leadership
Bibliographic record
Abstract
Background: Increasing data demonstrate a gender gap in career progression in neurology including underrepresentation of women in high impact neurology journal authorship and American Academy of Neurology recognition awards. We examined gender differences in leadership roles at the International Stroke Conference (ISC) from 2014-2018. Methods: As a retrospective analysis of conference data, this study was exempt from review by the institutional review board. Names of program committee members, early career development leaders, award recipients, invited speakers, and moderators for those sessions were taken from the Final Program from the 2014-2018 ISC. Conference leader gender was determined by name inspection and internet search. In a few instances of ambiguity, common gender association of the name was used. Self-reported ISC attendance demographics were obtained through the American Heart Association (AHA). Chi square test or Fisher exact test was used to compare demographics of invited leadership with attendance. Results: Between 29.9% and 44.7% of conference attendees were women. Attendees who did not disclose gender increased from 7.38% to 33.02%. Female membership on the program committee increased from 14.3% in 2014 to 48.2% in 2018. Women represented a minority of invited speakers, moderators and award recipients across all years, however, there were increases in female representation over time (see table). Conclusions: Women are less likely than men to hold leadership positions at the ISC. This is significant as such opportunities can play a valuable role in networking and career progression. It is reassuring that this gender gap has been decreasing over time, however, the AHA and program committee may wish to consider additional strategies to narrow this gap.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".