Awareness of Warning Symptoms of Heart Disease and Stroke: Results of a Follow-up Study of the Chinese Canadian Cardiovascular Health Project
Bibliographic record
Abstract
Background Our original pilot study in 2008 demonstrated a poor degree of awareness of heart disease and stroke among Chinese Canadians, warranting an updated survey of their knowledge. We sought to determine the current degree of knowledge of cardiovascular disease, including stroke, among ethnic Chinese residents of Canada. Methods A 35-question online survey was conducted in the fall of 2017 among 1001 Chinese Canadians (aged ≥ 18 years) in the greater Toronto area (n = 501) and Vancouver (n = 500). Knowledge of heart disease and stroke, such as signs and symptoms of stroke and heart attack, health habits, and initial response to a cardiovascular emergency were assessed. Results A total of 52.0% of the respondents were female, and 46.3% were aged <45 years. A total of 40.1% spoke Cantonese, and 23.7% spoke Mandarin; 79.5% were immigrants, and 31% had lived in Canada < 10 years. A total of 85% identified at least one heart attack symptom, and 80% identified at least one stroke symptom; 86.2% indicated that they would call 911 if experiencing a heart attack or stroke. Internet use was positively associated with the ability to identify a greater number of heart attack and stroke symptoms, compared to the number among non–Internet users ( P < 0.001). Women were 14% more likely to overlook gender as a risk factor for cardiovascular disease (CVD). Conclusions This study found that in 2017, compared to 2008, awareness of symptoms of heart disease and stroke improved among Chinese Canadians residing in Toronto and Vancouver.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".