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Record W2145793974 · doi:10.1504/ijram.2009.023158

Public perception of population health risks in Canada: health hazards and health outcomes

2009· article· en· W2145793974 on OpenAlexaffabout
Daniel Krewski, Louise Lemyre, Michelle C. Turner, Jennifer E. C. Lee, Christine Dallaire, Louise Bouchard, Kevin Brand, Pierre Mercier

Bibliographic record

VenueInternational Journal of Risk Assessment and Management · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of OttawaInstitute of Population and Public Health
Fundersnot available
KeywordsWorryEnvironmental healthRisk perceptionPerceptionPublic healthRecreationPsychologyPopulationOccupational safety and healthMedicineGerontologyAnxietyPolitical sciencePsychiatryNursing

Abstract

fetched live from OpenAlex

The focus of this article is a descriptive account of the perceptions of five health hazards (motor vehicles, climate change, recreational physical activity, cellular phones, and terrorism) and five health outcomes (cancer, long-term disabilities, asthma, heart disease, and depression) from a recent survey of 1503 Canadians. In an attempt to shed light on factors that influence risk perception in Canada, the extent to which these exemplars are perceived as high in risk and controllability, as well as the extent to which knowledge and uncertainty surrounding them is high, was examined. The degree to which these exemplars are deemed acceptable and generate worry among Canadians was also examined. Variation was observed in the extent to which different health hazards and outcomes are perceived on the various dimensions. Perceptions of health hazards and outcomes also vary significantly by gender, age, and education. Findings are compared to existing research on risk perception.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.077
GPT teacher head0.439
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2009
Admission routes2
Has abstractyes

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