Factor analytic investigation of Canadians' population health risk perceptions: the role of locus of control over health risks
Bibliographic record
Abstract
To better understand how health risks are conceptualised by the Canadian public, exploratory and confirmatory factor analytic techniques were applied to data from a recent national telephone survey on health risk perception (N = 1503). Hazards assessed comprised an array of 30 items selected a priori by a panel of experts to represent the following five determinants of population health: the physical environment, biology, lifestyle, the social environment and healthcare. Respondents in the survey rated each hazard in terms of perceived risk to the health of Canadians. Rather than the hypothesised five-factor model, findings supported a three-factor model, with biochemical, lifestyle and social health risk perceptions emerging as key factors explaining the public's health risk perceptions. Although the observed model differed from expectations, it maintained some elements of current population health models. Further analyses revealed that biochemical, lifestyle and social health risk perceptions were differentially associated with beliefs about the locus of control over health risks. Findings are contrasted with those of a similar analysis of data from a comparable national survey conducted in Canada in 1992, and are discussed in relation to trends in discourse on health risk over the past decade.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 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".