Public perceptions of coronary events risk factors: a discrete choice experiment
Bibliographic record
Abstract
OBJECTIVES: To assess public perceptions of coronary heart disease (CHD) risk factors. DESIGN: Discrete choice experiment questionnaire. SETTING: Six provincial centres in Northern Ireland. PARTICIPANTS: 1000 adults of the general public in Northern Ireland. PRIMARY AND SECONDARY OUTCOMES: The general public's perception of CHD risk factors. The effect of having risk factor(s) on that perception. RESULTS: Two multinomial logit models were created. One was a basic model (no heterogeneity permitted), while the other permitted heterogeneity based on respondents' characteristics. In both models individuals with very high cholesterol were perceived to be at the highest risk of having a coronary event. Respondents who reported having high cholesterol perceived the risk contribution of very high cholesterol to be greater than those who reported having normal cholesterol. Similar findings were observed with blood pressure and smoking. Respondents who were male and older perceived the contribution of age and gender to be lower than respondents who were female and younger. CONCLUSIONS: Respondents with different risk factors perceived such factors differently. These divergent perceptions of CHD risk factors could be a barrier to behavioural change. This brings into focus the need for more tailored health promotion campaigns to tackle CHD.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".