Attitude to health risk in the Canadian population: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Risk is a ubiquitous part of health care. Understanding how people respond to risks is important for predicting how populations make health decisions. Our objective was to seek preliminary descriptive insights into the attitude to health risk in the Canadian population and factors associated with heterogeneity in risk attitude. METHODS: We used a large market-research panel to survey (in English and French) a representative sample of the Canadian general population that reflected the age, sex and geography of the population. The survey included the Health-Risk Attitude Scale, which predicts how a person resolves risky health decisions related to treatment, prevention of disease and health-related behaviour. In addition, we assessed participants' numeracy and risk understanding, as well as income band and level of education. We summarized the responses, and we explored the independent associations between demographics, numeracy, risk understanding and risk attitude in multivariable models. RESULTS: Of 6780 respondents, 4949 (73.0%) were averse to health risks; however, but there was considerable heterogeneity in the magnitude of risk aversion. We found significant gradients of risk averse attitudes with increasing age and being female (p < 0.001) using the multivariable model. French-speaking participants appeared to be more risk averse than those who were English-speaking (p < 0.001), as were individuals scoring higher on the Subjective Numeracy Scale (p < 0.001). INTERPREATION: In general, Canadians were averse to health risks, but we found that a sizeable, identifiable group of risk takers exists. Heterogeneity in preferences for risk can explain variations in health care utilization in the context of patient-centred care. Understanding risk preference heterogeneity can help guide policy and assist in patient-physician decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".