Survey of public knowledge, attitudes, and practices regarding personal protection against COVID-19 in the post-pandemic era - Luo, Shixian; Xie, Jing; Chen, Jie; Li, Hongyu; Zhang, Sining (Frontiers in Psychology, 2024)
In the emerging post-pandemic era (the ‘wavelet’ era), humans must coexist with viruses for the foreseeable future, and personal protective behaviors will largely replace national-level preventive measures. In this new normal, encouraging the public to implement proper personal protective behaviors against the coronavirus disease (COVID-19) is vital to the sustainable development of cities and communities. This Knowledge–Attitude–Practice (KAP) survey conducted in Chengdu (N = 900) narrowed the knowledge gap regarding post-pandemic public practices of protective behavior. Findings show that:1) approximately 1/3 of the respondents are currently not concerned about COVID-19 at all; 2) respondents with different demographics and individual COVID-19-related factors showed significant differences in practice behaviors indoors and outdoors; 3) vulnerable groups performed better in practice behavior indoors/outdoors; 4) because the public may relax their vigilance outdoors, public places may become a transmission threat in the next outbreak; 5) attitudes are important, but limited incentives for practice; and 6) when knowledge increases beyond a threshold (68.75%-75% in this study), protective behaviors decrease. Our results suggest that authorities must continue to educate and motivate the public, extending measures to cover personal protective practices, and have targeted policies for specific demographics to ensure equity in healthcare in the event of another pandemic (COVID-19 and alike crisis). Besides, comparing the results of the current study with similar studies conducted in other parts of the world can provide insights into how different populations respond to and adopt COVID-19 protective behaviors. The epidemiologists can use the data collected by this and other KAP surveys to refine epidemiologic models, which can help predict the spread of the virus and the impact of interventions in different settings.