Prevalence of Sex-Specific Cardiovascular Disease Risk Factors, Medical Risk, and Engagement in Health-Promoting Behaviours in Premenopausal Females
Bibliographic record
Abstract
Background: Several sex-specific risk factors (SS-RFs) increase a women's risk for cardiovascular disease (CVD) but are often overlooked during risk assessment. The purpose of this study was to identify the prevalence of SS-RFs and assess CVD risk, knowledge, perceptions and behaviours in premenopausal Canadian women. Methods: An online survey was distributed across Canada to premenopausal biological females (19-49 years of age). The survey gathered demographics, medical history, engagement in health-promoting behaviours, and knowledge and perceptions of CVD risk. CVD risk was calculated using medical risk and SS-RFs were tabulated from medical history. Results: A total of 2559 participants (33 ± 8 years) completed the survey. The majority of our sample (82%) was classified as low medical risk. Of those classified as low risk, 35% had at least 1 SS-RF. Of high-risk individuals, 70% underestimated their risk, 21% of whom perceived themselves as low risk. Engagement in health behaviours was suboptimal. Knowledge of traditional CVD risk factors and prevention was relatively high; however, less than one-half were aware of SS-RFs such as early menopause (39.4%). Conclusions: Considering both traditional and SS-RFs, 47% of premenopausal Canadian women may be at risk for developing CVD. Of those deemed low medical risk for developing CVD, more than one-third reported having at least 1 SS-RF. Canadian women have poor knowledge of the risks associated with SS-RFs, lack sufficient awareness of the need for prevention of CVD, and are not engaging in sufficient health-promoting behaviours to mitigate future CVD risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".