Public psychological distance and spatial distribution characteristics during the COVID-19 pandemic: a Chinese context

• Published in: Current psychology (New Brunswick, N.J.) Vol. 41; no. 2; pp. 1065 - 1084
• Main Authors: Wu, Meifen, Long, Ruyin, Chen, Hong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer; Springer Nature B.V
• Subjects: Behavioral Science and Psychology; Coronaviruses; COVID-19; Epidemics; Forecasts and trends; Health aspects; Pandemics; Psychological aspects; Psychology; Social aspects; Social distance; Social psychology; Social Sciences; China; COVID-19; Spatial distribution characteristics; Psychological distance; China
• ISSN: 1046-1310; 1936-4733
• DOI: 10.1007/s12144-021-01861-x

The COVID-19 pandemic is a public health emergency, which continues to have a significant impact on the functioning of society and the public’s daily life. From the perspective of psychological distance (PD), this study used descriptive, differential, and spatial autocorrelation analysis methods to explore the cognitive distance, emotional distance, expected distance and behavioral distance of the Chinese public in relation to the COVID-19 pandemic. An analysis of 4042 valid sample data found that: (1) The event emotional distance and subject emotional distance were both furthest from the event and subject psychological distance dimensions, and anger about the event was the strongest. (2) The government was the most appealing subject in the process of pandemic prevention and control, but at the same time, the public’s sense of closeness to the government was also lower than that of the other three subjects, e.g., medical institutions. (3) Different pandemic regions showed significant differences in PD. Mean scores of PD in each risk region were as follows: High-risk regions > medium-risk regions > low-risk regions. (4) From a global perspective, no spatial autocorrelation was found in PD. However, from a local perspective, high-value regions (provinces with distant PD) are mainly concentrated in the southern regions (Guizhou, Guangxi, Hainan, Jiangxi), and low-value regions (provinces with close PD) are mainly concentrated in North China (Shanxi, Hebei, Beijing). Combined with the relevant conclusions, this paper put forward policy recommendations.