Integration of Women’s Cardiovascular Health Content Into Healthcare Provider Education: Results of a Rapid Review and National Survey
Bibliographic record
Abstract
Despite its importance, formal education in healthcare training programs on sex- and gender-specific cardiovascular disease (CVD) risk factors, symptoms, treatment, and outcomes is lacking. We completed rapid reviews of the academic and grey literature to describe the current state of women-specific CVD education in medical, nursing, and other healthcare education programs. Second, we analyzed results from a Canada-wide survey of healthcare professional education programs to identify gaps in curricula related to sex- and gender-specific training in CVD. Our academic review yielded only 15 peer-reviewed publications, and our online search only 20 healthcare education programs, that note that they specifically address women, or sex and gender, and CVD in their curricula. Across both searches, the majority of training and education programs were from the USA, varied greatly in length, delivery mode, and content covered, and lacked consistency in evaluation. Of surveys sent to 213 Canadian universities and other entry-to-practice programs, 80 complete responses (37.6%) were received. A total of 47 respondents (59%) reported that their programs included women-specific CVD content. Among those programs without content specific to CVD in women, 69.0% stated that its inclusion would add "quite a bit" or "a great deal" of value to the program. This study highlights the emerging focus on and substantial gaps in women-specific CVD training and education across healthcare education programs. All medical, nursing, and healthcare training programs are implored to incorporate sex- and gender-based CVD content into their regular curricula as part of a consolidated effort to minimize gaps in cardiovascular care.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".