Women's Heart Health: Knowledge, Beliefs, and Practices of Canadian Physicians
Bibliographic record
Abstract
BACKGROUND: Heart disease is a leading cause of morbidity and mortality in women. To date, the majority of knowledge regarding heart disease is based on research conducted in men. As a result, a male-oriented model of heart disease constitutes the basis for diagnostic and therapeutic strategies for both sexes. This article reports findings from the first survey of Canadian physicians to examine their knowledge, beliefs, and practices regarding heart disease in women. MATERIALS AND METHODS: This cross-sectional survey, adapted from an instrument used in the United States, was undertaken in the spring of 2015. A sample of 504 physicians from a randomly selected sample of online responses was produced. RESULTS: Overall, physician responses demonstrate a general lack of awareness regarding the prevalence and approaches to the identification of, and treatments for, heart disease in women. In addition, physicians did not provide high ratings of their own effectiveness in supporting female patients to prevent or manage heart disease. The barriers that physicians face and the strategies to support them in improving women's heart health were explored. CONCLUSIONS: There is a clear need to educate physicians about heart disease in women and its prevention and management. More female-specific research, prevention, and clinical programs will enhance our ability to significantly improve cardiovascular health in Canadian women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".