Threat appraisal, self-efficacy, and risk information seeking: a study on the behavior of not eating wild mushrooms in China
Bibliographic record
Abstract
BACKGROUND: We applied and extended the Protection Motivation Theory to investigate the predictive factors of not eating wild mushrooms to find ways to prevent food poisoning accidents. METHODS: A total of 322 residents from poisoning risk areas in China completed a self-administered questionnaire based on Protection Motivation Theory, assessing perceived susceptibility, rewards, self-efficacy, and risk information seeking. Data were analyzed using the partial least squares method. RESULTS: Perceived susceptibility (β = 0.221, P < 0.001), self-efficacy (β = 0.194, P < 0.001), and risk information seeking (β = 0.245, P < 0.001) were positively correlated with the intention of not eating wild mushrooms. In contrast, intrinsic rewards (β=-0.168, P = 0.039) were negatively correlated with the intention of not eating wild mushrooms. CONCLUSION: Perceptual susceptibility, intrinsic rewards, self-efficacy, and risk information seeking to predict the behavioral intention of not eating wild mushrooms. The results of this study support the theory of protection motivation and its extended applicability, providing evidence that public health crises can promote changes in resident behavior related to food safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".