Incorporating a Women’s Cardiovascular Health Curriculum Into Medical Education
Bibliographic record
Abstract
Despite cardiovascular disease (CVD) being the leading cause of death of women globally, research on CVD over the past several decades has focused primarily on men. CVD research has led to progress in diagnosis and treatment, medical education, and public awareness; however, few of these advances have applied specifically to women's cardiovascular health. There is a paucity of sex- and gender-specific educational material regarding CVD in clinical training programs for physicians. The irregularity in integrated curricula across medical schools in Canada may be a factor in persistent disparities in clinical care and outcomes experienced by women, compared with men. In response to this gap, the Training and Education Working Group of the Canadian Women's Heart Health Alliance undertook the planning, development, and dissemination of a Canadian Women's Heart Health Education Course. The development of the course was guided by a 6-step approach for curriculum development for medical education, which included conducting a needs assessment, determining and prioritizing content, setting goals and objectives, selecting educational strategies, implementation, and evaluation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".