Gender Differences in Barriers to Academic Cardiovascular Careers in North America
Bibliographic record
Abstract
BACKGROUND: Despite improvements in gender representation across cardiovascular training stages, there is a decline in women cardiologists at higher levels of academia. OBJECTIVES: The purpose of this study was to understand personal and systemic contributors to academic career attrition from the perspectives of men and women across career stages. METHODS: The American College of Cardiology administered a 24-question online survey to early career (EC) cardiologists and fellows in training (FITs) in the United States and Canada from August to September 2023 to assess barriers to academic careers. Responses were evaluated by self-reported gender. RESULTS: Among 608 respondents (16% response rate, 23.2% women), EC women and men shared similar reasons for being interested in academia. More women reported discrimination and competing clinical responsibilities as barriers to academia compared to men (25.6% vs 11.2%, and 60.5% vs 42%, respectively, P < 0.001 for both), while more men cited lack of job openings compared to women (34.8% vs 22.1%, respectively; P = 0.020). There was no difference in work satisfaction between women and men FITs, but women were less likely to report strong work satisfaction compared to men at the EC stage (62.3% vs 76.7%, respectively; P = 0.035). Both men and women ranked new methods to measure and reward academic pursuits as the most desired intervention to promote academic success, with more EC women valuing mentorship (83.3% vs 63.2%; P = 0.016). CONCLUSIONS: FIT and EC cardiologists' perspectives regarding academic cardiology careers reveal unique barriers by gender and career stage. These highlight the need to support the evolving needs of FITs and ECs during this vulnerable transition period.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".