Back

Research on Public Risk Perception and Panic Psychology in the Post-Emergency Phase of Major Public Health Crises

Shi, G.; Men, X.; Li, J.; Huang, C.; Ma, l.; Xia, C.

2025-05-02 public and global health
10.1101/2025.04.29.25325326 medRxiv
Show abstract

This study conducted a questionnaire survey among 1,605 citizens across 32 provinces in China to examine public risk perception and panic psychology regarding COVID-19 pandemic information. A predictive model of risk cognition and psychological behaviors was established to analyze public risk perception characteristics and behavioral responses during the late stages of the pandemic. Findings revealed that government preventive measures had the strongest impact on public risk perception, significantly surpassing the effects of infection-related information, recovery-related data, and personally relevant information. Compared to the 2020 risk perception map, dimensions such as "cure rates" and "post-recovery contagiousness" shifted from unfamiliar and uncontrollable to familiar and controllable, indicating substantial improvements in public risk cognition. While public risk awareness and fear levels decreased significantly compared to the initial pandemic phase, perceived control over "post-recovery health impacts" remained insufficient. Empirical analysis demonstrated that both positive and negative pandemic information could elevate public risk perception, which subsequently influenced psychological behaviors through the mediating role of risk cognition. Furthermore, the moderating effect of psychological tension underwent significant changes as the pandemic evolved, highlighting the dynamic adaptability of public socio-psychological and behavioral patterns during major public health crises. This study provides empirical evidence and scientific insights for risk communication strategies in the post-pandemic era.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.