Persistent Imbalance
Bibliographic record
Abstract
BACKGROUND: Despite recent gender parity of physicians entering pediatric cardiology, representation of women leaders lags their male colleagues. OBJECTIVES: We sought to better understand the variation in women in leadership roles in pediatric cardiology. METHODS: The gender of physicians in 16 prespecified leadership positions was collected by survey between July 2022 and January 2023 from pediatric cardiology programs with >5 cardiologists in North America. We analyzed the association of women leaders with center size (based on surgical volume), geographic region, presence of categorical fellowship program, and gender of division chief and department chair. RESULTS: Across 99 centers, a median of 13 (Q1-Q3: 10-15) roles/center were identified. Women held 36.8% of all leadership roles and 35.1% of cardiology-specific roles. Only 13% of pediatric cardiology chiefs were women. Their programs had more women in subsection leadership roles than male-led centers (47% vs 36%, P = 0.028). A minority of leadership posts were shared among 2 physicians, yet more women than men shared their roles (5.4% women vs 2.5% men, P = 0.010). More men than women have dual leadership positions (15.1% men vs 9.9% women, P = 0.012). We found no association of center size, geographic region, presence of fellowship program, or gender of department chair with percent women leadership. CONCLUSIONS: Women hold fewer leadership positions across most subsections of pediatric cardiology programs, with more equitable distribution at centers led by women division chiefs. Women are more likely to share a leadership position with another cardiologist and less likely than men to hold more than 1 leadership post concurrently.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.191 | 0.057 |
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".