A report from the Irish women in cardiology survey, exploring Europe’s largest gender gap in cardiology
Bibliographic record
Abstract
Aims: In Ireland, 8% of public cardiology consultants are female; this is the lowest proportion in Europe. We sought to understand perceptions amongst Irish trainees and consultants regarding aspects of working in cardiology in order to identify areas that can be targeted to improve gender equality. Methods and Results: In September 2021, the Irish Cardiac Society distributed a questionnaire to trainees and consultants in the Republic and Northern Ireland. Ethical approval was obtained from the University College Dublin, Ireland. There were 94 respondents (50% male, 50% consultants) which equates to ∼30% of all trainees and consultants in all Ireland. Although females were more likely to be single, overall, they had additional child-care responsibilities compared with male counterparts. Despite 53% of the respondents preferring to work less than full time, 64% reported a perceived lack of support from their departments. Males were significantly more likely to go into procedural/high radiation sub-specialities. Bullying was reported by 53% of females. Almost 80% of females experienced sexism and 30% reported being overlooked for professional advancement based on their sex. Females also rated their career prospects lower than males. Key challenges for women were: sexism, maternity leave/child-care responsibilities, cardiology as a 'boys club' and lack of flexible training. There was interest from both males and females in a mentorship programme and support for women in leadership positions. Conclusion: Discrimination including sexism, bullying, and equal opportunity for professional advancement are key aspects that need to be addressed to improve gender balance in cardiology within Ireland and Northern Ireland.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".