Min Zhou1, Wei Guo2. 1. Department of Sociology, Faculty of Social Sciences, the University of Victoria, Victoria, BC, V8W 3P5, Canada. 2. Department of Social Work and Social Policy, School of Social and Behavioral Sciences, Nanjing University, Jiangsu Province, 210023, P. R. China; The Centre for Asia-Pacific Development Studies, Nanjing University, Jiangsu Province, 210023, P. R. China. Electronic address: weiguo@nju.edu.cn.
Abstract
BACKGROUND: The COVID-19 pandemic generates negative psychological effects such as distress. Social influences on subjective distress associated with COVID-19 remain understudied in the Chinese context. Wuhan with its surrounding areas in Hubei province was not only the locale where first COVID-19 cases were detected in the world but was also the hardest hit across China. Data from Hubei provide a unique opportunity to investigate COVID-19-related subjective distress and its social correlates. METHODS: We use original data (N=3,465) from the General Social Survey on COVID-19 in Hubei, China, conducted in August 2020. Regression analysis is employed to examine the impact of socioeconomic status, family structure, and social policies on COVID-19-related subjective distress measured by the Impact of Event-Scale-Revised (IES-R). RESULTS: First, individuals with higher socioeconomic status are not more immune to distress, and actually it is those better-educated ones who are more distressed. Second, family structure influences distress. Divorced and widowed individuals are more prone to distress than those who are married or single. Those living with COVID-19-infected family members or living with a larger family are particularly more distressed. Third, stricter lockdown measures promote real and perceived protection and also increase individuals' psychological distance from the disease, thereby reducing subjective distress. LIMITATIONS: The sample is not totally random so we should use caution when generalizing the findings to the general population. CONCLUSIONS: The findings contribute to our understanding of mental health disparity during the COVID-19 pandemic. Certain social groups are at a higher risk of distress than others.
BACKGROUND: The COVID-19 pandemic generates negative psychological effects such as distress. Social influences on subjective distress associated with COVID-19 remain understudied in the Chinese context. Wuhan with its surrounding areas in Hubei province was not only the locale where first COVID-19 cases were detected in the world but was also the hardest hit across China. Data from Hubei provide a unique opportunity to investigate COVID-19-related subjective distress and its social correlates. METHODS: We use original data (N=3,465) from the General Social Survey on COVID-19 in Hubei, China, conducted in August 2020. Regression analysis is employed to examine the impact of socioeconomic status, family structure, and social policies on COVID-19-related subjective distress measured by the Impact of Event-Scale-Revised (IES-R). RESULTS: First, individuals with higher socioeconomic status are not more immune to distress, and actually it is those better-educated ones who are more distressed. Second, family structure influences distress. Divorced and widowed individuals are more prone to distress than those who are married or single. Those living with COVID-19-infected family members or living with a larger family are particularly more distressed. Third, stricter lockdown measures promote real and perceived protection and also increase individuals' psychological distance from the disease, thereby reducing subjective distress. LIMITATIONS: The sample is not totally random so we should use caution when generalizing the findings to the general population. CONCLUSIONS: The findings contribute to our understanding of mental health disparity during the COVID-19 pandemic. Certain social groups are at a higher risk of distress than others.
Authors: Argyroula E Kalaitzaki; George Tsouvelas; Alexandra Tamiolaki; George Konstantakopoulos Journal: Int J Ment Health Nurs Date: 2021-10-17 Impact factor: 5.100