School of Medicine and Health Management, Huazhong University of Science and Technology, No. 13 Hangkong Road, Qiaokou District, Wuhan, 430030, China.
School of Management, Huazhong University of Science and Technology, Wuhan, China.
BMC Public Health. 2021 Sep 25;21(1):1743. doi: 10.1186/s12889-021-11823-4.
With the spread of vaccines, more and more countries have controlled the outbreak of the COVID-19. In this post-epidemic era, these countries began to revive their economy. However, pollution remains in the environment, and people's physical and psychological health has been under threat due to some over-prevention behaviors. Instruments for governmental agencies to manage these behaviors are not yet available. This study aims to develop a measurement model to identify and measure the degree of over-prevention behaviors during the COVID-19 epidemic in China.
A survey online was conducted to collect cognition from 1528 Chinese people, including descriptions of various over-prevention behaviors defined by health authorities. Factor analyses were used to develop the measurement model and test its validity. Logistic regression analyses were conducted to explore demographic characteristics, indicating people who are inclined to exhibit over-prevention behaviors.
Four main factors were extracted to develop the model (eigenvalue = 7.337, 3.157, 1.447, and 1.059, respectively). The overall reliability (Cronbach's α = 0.900), the convergent (AVE > 0.5, CR > 0.8 for each factor) and discriminant validity is good. There is also a good internal consistency among these factors (Cronbach's α = 0.906, 0.852, 0.882, and 0.763, respectively). In Factor 1, gender has a negative effect (Beta = - 0.294, P < 0.05, OR = 0.745), whereas employment has a positive effect. Workers in institutions exhibit the greatest effect (Beta = 0.855, P < 0.001, OR = 2.352). In Factor 2, employment has a negative effect, with workers in institutions exhibit the greatest role (Beta = - 0.963, P < 0.001, OR = 0.382). By contrast, education level has a positive effect (Beta = 0.430, P < 0.001, OR = 1.537). In Factor 3, age plays a negative role (Beta = - 0.128, P < 0.05, OR = 0.880).
People show a discrepancy in the cognition toward various over-prevention behaviors. The findings may have implications for decision-makers to reduce the contradiction between the epidemic and economic revival via managing these behaviors.
随着疫苗的普及，越来越多的国家已经控制了 COVID-19 的爆发。在后疫情时代，这些国家开始重振经济。然而，环境污染依然存在，由于一些过度预防行为，人们的身心健康受到威胁。政府机构管理这些行为的手段尚不完善。本研究旨在开发一种测量模型，以识别和衡量中国 COVID-19 疫情期间过度预防行为的程度。
通过在线调查收集了 1528 名中国人的认知数据，包括卫生部门定义的各种过度预防行为的描述。采用因子分析方法开发测量模型并检验其有效性。进行逻辑回归分析以探讨人口统计学特征，表明哪些人更倾向于表现出过度预防行为。
提取了四个主要因素来构建模型（特征值分别为 7.337、3.157、1.447 和 1.059）。模型的整体可靠性（Cronbach's α=0.900）、收敛性（每个因素的 AVE>0.5，CR>0.8）和区分效度良好。这些因素之间也具有良好的内部一致性（Cronbach's α分别为 0.906、0.852、0.882 和 0.763）。在因素 1 中，性别有负向影响（Beta=-0.294，P<0.05，OR=0.745），而就业有正向影响。机构工作者表现出最大的影响（Beta=0.855，P<0.001，OR=2.352）。在因素 2 中，就业有负向影响，机构工作者表现出最大的作用（Beta=-0.963，P<0.001，OR=0.382）。相比之下，教育水平有正向影响（Beta=0.430，P<0.001，OR=1.537）。在因素 3 中，年龄起负向作用（Beta=-0.128，P<0.05，OR=0.880）。
人们对各种过度预防行为的认知存在差异。研究结果可能对决策者通过管理这些行为来减少疫情与经济复苏之间的矛盾具有启示意义。
