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Am J Infect Control . Motivating Factors of Compliance to Government's COVID-19 Preventive Guidelines: An Investigation using Discrete Choice Model

tetano

Editor, Senior Moderator
Am J Infect Control


. 2023 Jan 10;S0196-6553(23)00001-9.
doi: 10.1016/j.ajic.2022.12.013. Online ahead of print.
Motivating Factors of Compliance to Government's COVID-19 Preventive Guidelines: An Investigation using Discrete Choice Model


Sol Kwon[SUP] 1 [/SUP], Hae-Sun Suh[SUP] 2 [/SUP], Chung-Mo Nam[SUP] 3 [/SUP], Hye-Young Kang[SUP] 4 [/SUP]



Affiliations

Abstract

Background: The spread of coronavirus disease 2019 (COVID-19) has resulted in a worldwide pandemic. We aimed to identify the factors that motivate public compliance with the government's COVID-19 preventive recommendations.
Methods: Focus group interviews were conducted to identify influencing factors. The relative importance of each factor was investigated through a survey, based on a discrete choice model, from February to June, 2021 in South Korea.
Results: "Severity of COVID-19 symptoms" (relative importance [magnitude of attribute coefficients]: 28.40%) and "risk of infection" (27.50%) were the most influential health-related factors, followed by social consequences of infection, including "cessation of social activities due to self-quarantine" (19.77%), "risk of personal information being disclosed when infected and social criticism on the infected person" (15.78%), and "risk of spreading infection" (8.55%). Respondents behaved differently based on their socioeconomic characteristics and COVID-19 experience.
Discussion: The perceived severity of symptoms was a strong motivator among fragile individuals, such as women and older adults. "Cessation of social activities" was the most influential factor for those infected with COVID-19, while "risk of infection" was for those whose acquaintances were infected.
Conclusions: The provision of information regarding COVID-19 to the public must be tailored based on an understanding of behavioral differences.

Keywords: COVID-19; discrete choice model; prevention.
 
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