Identifying symptom communities and core symptoms in the anxiety-depression network among computer science students

Abstract Although the mental health of college students has become a focus in the health field and society, there is still little discussion about the mental health of students in computer science and related majors. This study was guided by the network theory of mental disorders, presents a symptom network analysis of anxiety and depression among computer science students. A total of 3934 computer science students were included in this study. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and the nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depression symptoms. The connection between Nervousness and Uncontrollable worry is the strongest edge in the network. We identified the three core symptoms with the highest node strength were concentration, fatigue and psychomotor problems. The three bridge symptoms with the highest bridge strength were irritability, feeling afraid and psychomotor problems. Four well-characterized symptom communities were identified through the SpinGlass algorithm, including the core anxiety symptom community, the anxiety somatization manifestation symptom community, the core depressive symptom community, and the depressive physiological manifestation symptom community. The network performed well in both stability and accuracy tests. These findings are important for future interventions and improving the role of mental health issues for students with diverse majors and stressors.