ArticleCurrent psychology (New Brunswick, N.J.)2022
Public psychological distance and spatial distribution characteristics during the COVID-19 pandemic: a Chinese context.
Article in Current psychology (New Brunswick, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Exploring perceived learning effectiveness in virtual reality health communication through the lens of construal level theory.BMC public health · 2024Article
- How Does Psychological Distance Influence Public Risky Behavior During Public Health Emergencies.Risk management and healthcare policy · 2024Article
- Latent profile analysis of security among patients with COVID-19 infection in mobile cabin hospitals and its relationship with psychological capital.Frontiers in public health · 2022Article
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3 authors.
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Abstract
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.
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