Experiences and Impacts of Harassment and Discrimination Among Women in Cardiac Medicine and Surgery: A Single-Center Qualitative Study
Bibliographic record
Abstract
Background: Gender- and sex-based harassment and discrimination are consistently reported by about 50% of women physicians, and the prevalence may be even greater among women in cardiology. An exploration of these experiences and their impacts on women in healthcare is necessary to design interventions, create supports, and facilitate empathy, support, and allyship among leadership. Methods: To understand and describe the experiences of harassment and discrimination among women working in cardiac sciences, to inform the design of interventions and supports, we performed one-on-one, semi-structured interviews with women in the Department of Cardiac Sciences in a single institute. Interviews were coded independently in parallel using thematic analysis and reconciled by trained qualitative researchers. Experiences were categorized as harassment using the Canadian Human Rights Act. Codes were grouped into themes by iterative discussion. Results: There were 15 participants, including trainees, physicians in a variety of cardiac subdisciplines, and nurse practitioners. All participants had experienced sex- or gender-based discrimination at work, though the impact and perception of these experiences varied. Whereas some participants felt that these experiences had little influence on their careers or personal lives, others changed practice specialties or locations due to harassment. Several participants had been sexually assaulted at work. Interviews revealed modifiable barriers to reporting harassment. Conclusions: This qualitative dataset enriches the prevalence data on sex- and gender-based harassment among women working in cardiology by describing the impacts and perceptions of this harassment. Organizations should address commonly described barriers to reporting harassment, including addressing retaliation, and create systems-level supports for those affected by harassment.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".