The Role of Physical Activity Prescription in Cardiovascular Disease Prevention Amongst South Asian Canadians
Bibliographic record
Abstract
Unequivocal evidence suggests an increased prevalence of cardiovascular disease (CVD) amongst South Asian Canadians (SACs) compared to other ethnic cohorts, due to a combination of their unique cardiometabolic profile and environmental factors. This unfavorable CVD profile is characterized by an elevated risk of dyslipidemia, high apolipoprotein B/apolipoprotein A1 ratio, hypertension, glucose intolerance, type 2 diabetes mellitus, as well as increased BMI, body fat percentage, abdominal and visceral adiposity. Despite the overwhelming evidence for the effectiveness of physical activity (PA) in circumventing the onset of CVD and in the reduction of CVD risk factors, SACs are among the most physically inactive cohorts in Canada. This relates to a set of common and unique socio-cultural barriers, such as gender, beliefs and perceptions about illness, immigration, unfavorable PA environments, and their high prevalence of debilitating chronic diseases. Several strategies to improve PA participation rates in this high-risk population have been suggested, and include the implementation of culturally sensitive PA interventions, as well as clinician training in PA prescription through workshops that emphasize knowledge translation into clinical practice. Therefore, the purpose of this mini-review is to highlight and discuss: (1) the burden of heart disease in SACs (2) the cardiovascular benefits of PA for SACs; (3) factors affecting PA participation among SACs and how they can be addressed; (4) the impact of culturally sensitive PA prescription on CVD prevention; (5) barriers to culture-specific PA prescription by clinicians, and strategies to improve its use and impact.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".