Disaster medicine and public health preparedness · 2025
The Association between Intolerance of Uncertainty and Mental Health Symptoms among 86,767 Chinese College Students during the COVID-19 Pandemic: A Latent Profile Analysis.
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Ma Zijie, Chen Yujing, Deng Yishuai, Luo Cong, Liu Lili, Huang Andi, Wang Xiayong, Li Jiahong, Zhao Jingbo
Аннотация
OBJECTIVE: This study aimed to investigate the individual characteristics of intolerance of uncertainty (IU) and its association with mental health symptoms among Chinese college students during COVID-19.
METHODS: In total, 86,767 students completed the online survey in Guangdong province in June 2021. Data collected including socio-demographic and COVID-19-related information, IU, and mental health symptoms (depression, anxiety, insomnia, and suicidal ideation). Latent profile analysis was used to classify IU subgroups. Logistic regression was used to identify IU risk factors.
RESULTS: Four IU subgroups were identified, named low IU (n = 9,197, 10.6%), medium-low IU (n = 25,514, 29.4%), medium-high IU (n = 38,805, 44.7%), and high IU (n = 13,251, 15.3%). Scores of mental health symptoms varied from the degree of IU in the latent profiles. Mental health status was the worst in the high IU group. In addition, females, freshmen, and those perceiving more impacts from COVID-19 and spending longer time surfing COVID-19 information online were at risk of high IU.
CONCLUSIONS: Our findings showed that individuals differ in the total degree of intolerance of the uncertainties. Students with high IU were associated with worse mental health symptoms. Thus, taking actions to target individuals with high IU and developing their adaptive coping strategies are imperative during pandemics.
METHODS: In total, 86,767 students completed the online survey in Guangdong province in June 2021. Data collected including socio-demographic and COVID-19-related information, IU, and mental health symptoms (depression, anxiety, insomnia, and suicidal ideation). Latent profile analysis was used to classify IU subgroups. Logistic regression was used to identify IU risk factors.
RESULTS: Four IU subgroups were identified, named low IU (n = 9,197, 10.6%), medium-low IU (n = 25,514, 29.4%), medium-high IU (n = 38,805, 44.7%), and high IU (n = 13,251, 15.3%). Scores of mental health symptoms varied from the degree of IU in the latent profiles. Mental health status was the worst in the high IU group. In addition, females, freshmen, and those perceiving more impacts from COVID-19 and spending longer time surfing COVID-19 information online were at risk of high IU.
CONCLUSIONS: Our findings showed that individuals differ in the total degree of intolerance of the uncertainties. Students with high IU were associated with worse mental health symptoms. Thus, taking actions to target individuals with high IU and developing their adaptive coping strategies are imperative during pandemics.