Abstract 19107: Variation in Women's Representation Among Leadership Positions in Pediatric Cardiology Programs Across North America
Bibliographic record
Abstract
Introduction: Despite several years of gender parity entering the field of pediatric cardiology, representation of women leaders trails that of their male colleagues. We sought to better understand the variation in women in leadership roles in our field. Methods: Pediatric cardiology programs that participate in the Society of Thoracic Surgeons database with > 5 cardiologists were included. Data regarding gender of physicians in 16 leadership positions was collected. We analyzed the association of women in leadership roles with center size (based on surgical volume), geographic region, presence of fellowship, and gender of division chief and department chair. Results: Of the 99 centers in this study, a median of 13 (IQR 10-15) roles/center were identified, with 4 (IQR 3-6), 33.3% held by women. The lowest representation was in pediatric cardiology chiefs: 13% (table). Programs led by women chiefs had more women in leadership roles (48% vs 35%, p=0.01). In the US, the Northeast has more %women leaders than the West, South, or Midwest: 43% vs 33, 32, 34% respectively, p=0.035. Fellowship program or a woman department chair were not associated with %women leaders. The %women varied among the different sections: lowest in invasive subspecialties, interventional (17%) and EP (20%), and highest in fetal directors (66%). Fewer cardiac MRI leads in medium or high-volume centers are women compared to low volume centers (16 vs 39%, p=0.02), otherwise no volume-based differences were seen. Conclusions: Women hold fewer leadership positions across most subsections of pediatric cardiology programs, with more equitable distribution at centers led by women division chiefs. Future studies will define barriers to leadership equity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".