Application of the protection motivation theory in predicting wild mushroom consumption among university students in China
Bibliographic record
Abstract
BACKGROUND: Wild mushroom poisoning represents a significant public health challenge in China, with the highest mortality rate globally. Despite extensive prevention campaigns, consumption behaviors persist, particularly among university students who may be influenced by social media and peer pressure. OBJECTIVE: This study applied Protection Motivation Theory (PMT) to investigate psychological factors influencing wild mushroom consumption intentions among Chinese university students and identify key predictors for targeted intervention development. METHODS: A cross-sectional survey was conducted among 216 Chinese university students. The PMT model included threat appraisal (perceived severity, susceptibility, benefits, costs) and coping appraisal (response efficacy, self-efficacy, response costs). Behavioral intention was assessed through scenario-based consumption likelihood measures. Structural equation modeling was used to test the theoretical model. RESULTS: The PMT model demonstrated good fit (χ²/df = 2.14, CFI = 0.94, TLI = 0.92, RMSEA = 0.073, SRMR = 0.065) and explained 42.3% of the variance in wild mushroom consumption intentions (R² = 0.423, 95% CI [0.35, 0.49]). Perceived benefits emerged as the strongest positive predictor (β = 0.385, 95% CI [0.27, 0.50], p < 0.001), while self-efficacy was the strongest negative predictor (β = -0.298, 95% CI [-0.42, -0.18], p < 0.001). Traditional threat appraisal components (severity and susceptibility) showed minimal predictive effects. Response costs also significantly predicted consumption intentions (β = 0.156, 95% CI [0.04, 0.27], p < 0.01). CONCLUSIONS: PMT provides a valuable framework for understanding wild mushroom consumption behavior among Chinese university students. The dominance of perceived benefits and self-efficacy as predictors suggests that effective interventions should address positive outcome expectations while building confidence in avoidance behaviors. These findings indicate that effective interventions must move beyond traditional risk communication to address the complex interplay of perceived benefits, self-efficacy, and social factors driving consumption decisions, with implications for developing culturally-tailored, multi-component prevention strategies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".