Psychology Research and Behavior Management (Dec 2024)

The Relationship Between Cognitive Emotion Regulation Strategy and Mental Health Among University Students During Public Health Emergency: A Network Analysis

  • Li M,
  • Jia Q,
  • Yuan T,
  • Zhang L,
  • Wang H,
  • Ward J,
  • Jin Y,
  • Yang Q

Journal volume & issue
Vol. Volume 17
pp. 4171 – 4181

Abstract

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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 workCorrespondence: Qun Yang; Yinchuan Jin, Email [email protected]; [email protected]: 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

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