Pros and cons factors influence population attitudes toward non-pharmaceutical interventions and vaccination during post–COVID-19

Public Health(2022)

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摘要
Objectives Population compliance greatly influences the effectiveness of vaccination and non-pharmaceutical interventions (NPIs) for the curtaining of COVID-19 transmission. We aimed to determine the conceptual framework of potential factors that influence compliance. Study design This was a cross-sectional study. Methods Questionnaires were used to survey population attitudes toward vaccination and NPIs in China. Confirmatory factor analysis of the survey data by structural equation model was used to define the pros and cons factors of attitudes. The strength and direction of each factor’s effect on population attitudes were illustrated by Bayesian network analysis. Results A total of 1700 respondents aged 18–70 years were surveyed with a panel of 34 questionnaires. Of these questionnaires, the confirmatory factor and structural equation model analysis identified five categories contributing to positive attitudes, including response efficiency, willingness and behavior, trust, cues to action, and knowledge, as well as four categories contributing to negative attitudes, including autonomy, perceived barriers, threat, and mental status. Bayesian networks revealed that cues to action produced a driving force for positive attitudes, followed by willingness and behavior, trust, response efficiency, and knowledge, whereas perceived barriers produced a driving force for negative attitudes, followed by autonomy and threat. Conclusions This study established a concise and representative list of questionnaires that could be applied to investigate the conceptual framework of potential pros and cons factors of attitudes toward vaccination and NPIs for COVID-19 prevention. The factors with driving forces should be addressed with a priority to effectively improve population compliance.
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关键词
COVID-19,Non-pharmaceutical interventions,Vaccination,Structural equation model,Bayesian
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