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Revue internationale de santé et de sécurité publiques

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Perceptions and Behavioral Adoptions of the Preventive Strategies from Person-To-Person Transmission of COVID-19 among the Public in China: An Online Cross-Sectional Survey

Abstract

Xian Zhu, Yong Gan, Bo Wu, Qinyong Gou, Daikun Zheng, Chenglu Liu, Chang Xu, Zhou Tao, Yaoyue Hu, Hong Xu*, and Qian Long

Background and Objectives: COVID-19 is still spreading rapidly around the world. At present, the main strategies adopted to prevent spreading are quarantine, social distancing and isolation of infected cases. This study investigated perceptions and behavioural adoptions of COVID-19 prevention strategies among the Chinese public and identified factors predicting individual health behaviour.

Methods: We conducted a cross-sectional online survey between 22 February and 5 March, 2020. We approached to urban residents through snowball sampling method using the Chinese social media. The Health Belief Model (HBM) was adopted to guide the analysis. Bivariate and multivariate logistic regressions were used to examine impacts of modifying factors and individual beliefs on individual health behaviour.

Results: Of 5675 valid questionnaires, 95.8% of the respondents well understood the knowledge of preventive measures from COVID-19 transmission, while 79.9% of the respondents adopted the behaviour advised. Of which, the adoption of wearing face mask was the highest (98.5%). Multivariate logistic regression results showed that the respondents who were female, had better income, and better knowledge, perceived benefits, and did not feel anxious were more likely to adopt Behaviors advised.

Conclusion: The respondents of this study highly accepted and adopted behaviours advised to slow down the COVID-19 epidemic. The policy support should target on the social vulnerable groups. The psychological support should be disseminated through different means, and the consultation should be provided to those who are in need.

Avertissement: Ce résumé a été traduit à l'aide d'outils d'intelligence artificielle et n'a pas encore été examiné ni vérifié

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