RISK PERCEPTION/COMMUNICATION ARTICLE Exploring the Determinants of the Perceived Risk of Food Allergies in Canada
Bibliographic record
Abstract
Food allergies are emerging health risks in much of the Western world, and some evidence suggests prevalence is increasing. Despite lacking scientific con-sensus around prevalence and management, policies and regulations are being implemented in public spaces (e.g., schools). These policies have been criticized as extreme in the literature, in themedia, and by the non-allergic population. Backlash appears to be resulting from different perceptions of risk between different groups. This article uses a recently assembled national dataset (n = 3,666) to explore how Canadians perceive the risks of food allergy. Analyses revealed that almost 20% self-report having an allergic person in the household, while the average respon-dent estimated the prevalence of food allergies in Canada to be 30%. Both of these measures overestimate the true clinically defined prevalence (7.5%), indicating an inflated public understanding of the risks of food allergies. Seventy percent reported food allergies to be substantial risks to the Canadian population. Multivariate logis-tic regression models revealed important determinants of risk perception including
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".