Stroke-related knowledge, lifestyle behaviours and health beliefs in Singaporean Chinese: Implications for health education
Bibliographic record
Abstract
Objective: The objective of the present study was to describe stroke-related knowledge (risk factors, warning signs and emergency response), lifestyle behaviours and health beliefs among Singaporean Chinese, and to identify any factors associated with such knowledge, behaviours and beliefs. Design: This was a cross-sectional study design employing a non-probability sampling method. Setting: Participants were recruited from the community. Method: Singaporean Chinese aged 40–74 years completed an interviewer-administered questionnaire seeking demographic information, knowledge of stroke risk factors, warning signs and emergency response, lifestyle behaviours such as physical activity participation and dietary habits, and health beliefs. Results: A total of 411 questionnaires (42% men, average age 52.4 years ± 7.3) were analyzed. Most respondents were able to identify at least one correct risk factor and warning sign (88% and 78% respectively). But only 38% stated the correct emergency response. Mass media was the main source of their knowledge. Most respondents reported healthy lifestyle and have positive health beliefs, many of which were associated with age, gender, education, income, religion and whether having relatives suffering from a chronic illness. Conclusion: In conclusion, this survey identified areas for health education programmes to improve stroke-related knowledge and lifestyle behaviour change in different target groups.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".