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突发公共卫生事件下大学生情绪调节认知策略与心理健康的关系:网络分析
Authors Li M, Jia Q, Yuan T, Zhang L, Wang H , Ward J, Jin Y, Yang Q
Received 4 July 2024
Accepted for publication 6 November 2024
Published 8 December 2024 Volume 2024:17 Pages 4171—4181
DOI https://doi.org/10.2147/PRBM.S485555
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Gabriela Topa
Mengze Li,1,* Qiannan Jia,1,* Tifei Yuan,2 Lin Zhang,3 Huizhong Wang,1 Jamie Ward,4 Yinchuan Jin,1 Qun Yang1
1Department of Military Medical Psychology, Air Force Medical University, Chinese People’s Liberation Army (PLA), Xi’an, People’s Republic of China; 2Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China; 3Outpatient Department, 986th Hospital Affiliated to Air Force Medical University, Xi’an, People’s Republic of China; 4School of Psychology, University of Sussex, Brighton, UK
*These authors contributed equally to this work
Correspondence: Qun Yang; Yinchuan Jin, Email yangqun1125@hotmail.com; jyc_fmmu@163.com
Background: Public health emergencies pose threats to mental health, and cognitive emotional regulation can be a crucial coping strategy. This study explored the relationship between cognitive emotion regulation strategies and mental health among university students during the COVID-19 pandemic using network analysis.
Methods: 1100 university students completed questionnaires assessing depression, anxiety, somatization, and cognitive emotion regulation strategies. Network analysis was conducted to identify network structures and bridge symptoms.
Results: (1) In the depression network, the strongest edge is D1 (Little interest)-D2 (Feeling down), while D2 emerged as the node with the highest centrality. C1 (Self-blame), C8 (Catastrophizing), D6 (Feeling bad), and D9 (Suicide) are bridge symptoms. (2) In the anxiety network, A2 (Uncontrollable worrying)-A3 (Worrying too much) were identified as the strongest edge, and A2 exhibiting the highest centrality. C1 (Self-blame), C8 (Catastrophizing), and A6 (Easy annoyance) are bridge symptoms. (3) In the somatization network, the strongest edge is S14 (Fatigue)-S15 (Sleep disturbances) and S9 (Palpitations) exhibited the highest centrality. C1 (Self-blame), C3 (Rumination), C8 (Catastrophizing), S9 (Palpitations), and S14 (Fatigue) are bridge symptoms.
Conclusion: Self-blame and catastrophizing are important bridge symptoms for cognitive emotion regulation strategies and mental health networks, so cognitive behavioral therapy, focusing on self-blame and catastrophizing as intervention targets, could most effectively improve mental health during public health emergencies.
Keywords: cognitive emotion regulation, mental health, network analysis, public health emergency, COVID-19