The structure of Canadians' health risk perceptions: Environmental, therapeutic and social health risks
Bibliographic record
Abstract
Numerous studies have examined health risk perception through public ratings of health hazards, comparing them across lists, across time or across subpopulations. Yet, few have unveiled people's mental organization and representation of the factors affecting health risk. In order to better understand how the construct of health risk is conceptualized by the public, a principal components analysis was conducted on data from a previous national survey in which Canadians rated a series of hazards with respect to perceived level of health risk. Canadians conveyed their concerns as falling into three broad components: Environmental (e.g., nuclear waste, PCBs or Dioxins, etc.), Therapeutic (e.g., contact lenses, medical X-rays, etc.), and Social health risks (e.g., motor vehicle accidents, street crime, etc.). Generally, hazards perceived as posing the most health risk were those belonging to Social health risks. Perceptions of Environmental, Therapeutic and Social health risks were higher among women, respondents with lower education or income, and among residents of Québec. Results are discussed in relation to the population health approach (Evans et al. 1994 Evans, R. G., Barer, M. L. and Marmor, T. R. 1994. Why are Some People Healthy and Others Not?, New York: Aldine de Gruyter. [Crossref] , [Google Scholar]), in which the physical environment, biology, lifestyle, social environment and health care represent major determinants of the health of populations and population subgroups.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".