Background: 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.
Methods: 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.
Results: 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).
Conclusions: 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.
Keywords: COVID-19; Demographic characteristics; Epidemic prevention; Measurement model; Over-prevention behaviors.
© 2021. The Author(s).