Evidence map›Paper›PMID 41951655›Full record

ArticleScientific reports2026

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

Wei Yi, Kun Yang, Zhengfan Wei, Mohd Mahzan Awang, Wan Ahmad Munsif Wan Pa, Yonglin Chen, Meiyang Wang, Shuoyu Jing

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Wei YiCollege of Information Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China.
Kun YangCollege of Information Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China.
Zhengfan WeiCollege of Information Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China.
Mohd Mahzan AwangFaculty of Education, National University of Malaysia, Bangi, Malaysia.
Wan Ahmad Munsif Wan PaFaculty of Education, National University of Malaysia, Bangi, Malaysia.
Yonglin ChenFaculty of Education, National University of Malaysia, Bangi, Malaysia.
Meiyang WangCollege of Clinic, Sanquan College of Xinxiang Medical University, Xinxiang, China.
Shuoyu JingCollege of Information Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China. p119216@siswa.ukm.edu.my.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AnxietyDepressionStudentsAdolescentAdultComputersFemaleGeneralized Anxiety DisorderHumansMaleMental HealthSurveys and QuestionnairesYoung AdultAnxietyComputer science studentsDepressionPsychological network analysisSymptoms community

Identifiers

PMID41951655
PMCPMC13061885

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