Gender Differences in the Pursuit of Cardiac Electrophysiology Training in North America
Bibliographic record
Abstract
BACKGROUND: Despite the increase in the number of female physicians across most specialties within cardiology, <10% of clinical cardiac electrophysiology (EP) fellows are women. OBJECTIVES: This study sought to determine the factors that influence fellows-in-training (FITs) to pursue EP as a career choice and whether this differs by gender. METHODS: The authors conducted an online multiple-choice survey through the American College of Cardiology to assess the decision factors that influence FITs in the United States and Canada to pursue cardiovascular subspecialties. RESULTS: A total of 933 (30.5%) FITs completed the survey; 129 anticipated specializing in EP, 259 in interventional cardiology (IC), and 545 in a different field or were unsure. A total of 1 in 7 (14%) FITs indicated an interest in EP. Of this group, more men chose EP than women (84% vs 16%; P < 0.001). The most important factor that influenced FITs to pursue EP was a strong interest in the field. Women were more likely to be influenced by having a female role model (P = 0.001) compared with men. After excluding FITs interested in IC, women who deselected EP were more likely than men to be influenced by greater interest in another field (P = 0.004), radiation concerns (P = 0.001), lack of female role models (P = 0.001), a perceived "old boys' club" culture (P = 0.001) and discrimination/harassment concerns (P = 0.001). CONCLUSIONS: Women are more likely than men to be negatively influenced by many factors when it comes to pursuing EP as a career choice. Addressing those factors will help decrease the gender disparity in the field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".