Uncertainty Beyond Probabilities of BSE: Appraisals Predicting Worry and Coping Strategies in the Canadian Public
Bibliographic record
Abstract
The impact of bovine spongiform encephalopathy (BSE) is not limited to the infection with the BSE agent but also affects psychosocial responses, such as worry and loss of confidence in public authorities. It was shown in past crises that these reactions depended upon the way the event was perceived by the public. Understanding the nature of the perceptions of BSE is therefore of great importance for risk management in all phases of the risk, including the period before the onset of a crisis, when BSE is still only a pending threat to human health. This study analyzed data from a representative national survey of Canadians (n = 1,517) on the perceived risk of prion diseases. Factor analysis revealed emerging dimensions of BSE appraisals and regression analysis identified variables that predicted worry and coping strategies. Results yielded three significant factors, each relating differently to reactions to BSE: (1) Perceived impact, which combined perceived risk for health and likelihood of occurrence of BSE crises, was the main predictor of worry about eating tainted beef; (2) perceived mastery, consisting of personal knowledge and control, predicted taking action to avoid the disease; and (3) perceived intricacy, composed of perceived complexity and uncertainty, uniquely predicted trying to ignore BSE-related risks. Further regression analysis and analysis of variance exposed a moderating role of perceived intricacy on the relationship between perceived impact of BSE crises and worry. The implications of these findings for risk communication and management are described.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| 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".