Scholarlink

Objective <p>Based on network analysis methodology, a network model was constructed to elucidate the relationship between academic stress and depressive symptoms among middle school students. By identifying core nodes (high-strength symptoms) and bridge nodes (cross-group associated symptoms) within the two symptom clusters, the study reveals the interactive pathways between these constructs. This approach provides a foundation for precise mental health interventions tailored to middle school students.</p> Methods <p>A total of 1,576 middle school students in central Anhui Province were selected by the cluster sampling method, and questionnaire surveys were conducted using the Academic Stress Scale for Middle School Students and the Center for Epidemiologic Studies Depression Scale (CES-D). Data analysis was conducted using SPSS 26.0 and R software (Version 4.4.1). The partial correlation network was estimated using the EBICglasso algorithm (γ = 0.5) in R software (Version 4.4.1). Node strength centrality and bridge strength centrality were calculated, and the stability of the network was assessed using the correlation stability (CS) coefficient.</p> Results <p>The network structure of academic stress and depressive symptoms among middle school students comprises 308 valid edges, forming two tightly connected clusters. In the Academic Stress Scale, SS16 (strength = 1.169) and SS12 (strength = 1.136) were identified as core nodes; SS20 (bridge strength = 0.248) and SS14 (bridge strength = 0.185) exhibited higher bridge strengths, with SS20 (bridge strength = 0.248) serving as the critical bridge node.In the Depression Scale, CES-D6 (strength = 1.211) and CES-D3(strength = 1.189) exhibited higher strengths, while CES-D16(bridge strength = 0.189) and CES-D20(bridge strength = 0.157) showed higher bridge strengths. The cross-symptom clusters edge between SS20 (“When I am upset, I feel I have no close friends to talk to.“) and CES-D14(“I felt lonely.“) had the highest edge weight (0.137). The network stability test revealed that both strength centrality (CS = 0.75) and bridge strength centrality (CS = 0.67) exceeded the recommended threshold (&gt; 0.5).</p> Conclusion <p>Academic stress and depressive symptoms form a dynamic interconnected network through social isolation (SS20) and low life satisfaction (CES-D16), suggesting the need to design targeted intervention strategies focusing on bridge nodes to mitigate their negative impact on middle school students.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The relationship between academic stress and depressive symptoms in middle school students: a network analysis model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月14日

查看全文
  • Ke Zheng,
  • Zijun Chen,
  • Liang Yu

摘要

Objective

Based on network analysis methodology, a network model was constructed to elucidate the relationship between academic stress and depressive symptoms among middle school students. By identifying core nodes (high-strength symptoms) and bridge nodes (cross-group associated symptoms) within the two symptom clusters, the study reveals the interactive pathways between these constructs. This approach provides a foundation for precise mental health interventions tailored to middle school students.

Methods

A total of 1,576 middle school students in central Anhui Province were selected by the cluster sampling method, and questionnaire surveys were conducted using the Academic Stress Scale for Middle School Students and the Center for Epidemiologic Studies Depression Scale (CES-D). Data analysis was conducted using SPSS 26.0 and R software (Version 4.4.1). The partial correlation network was estimated using the EBICglasso algorithm (γ = 0.5) in R software (Version 4.4.1). Node strength centrality and bridge strength centrality were calculated, and the stability of the network was assessed using the correlation stability (CS) coefficient.

Results

The network structure of academic stress and depressive symptoms among middle school students comprises 308 valid edges, forming two tightly connected clusters. In the Academic Stress Scale, SS16 (strength = 1.169) and SS12 (strength = 1.136) were identified as core nodes; SS20 (bridge strength = 0.248) and SS14 (bridge strength = 0.185) exhibited higher bridge strengths, with SS20 (bridge strength = 0.248) serving as the critical bridge node.In the Depression Scale, CES-D6 (strength = 1.211) and CES-D3(strength = 1.189) exhibited higher strengths, while CES-D16(bridge strength = 0.189) and CES-D20(bridge strength = 0.157) showed higher bridge strengths. The cross-symptom clusters edge between SS20 (“When I am upset, I feel I have no close friends to talk to.“) and CES-D14(“I felt lonely.“) had the highest edge weight (0.137). The network stability test revealed that both strength centrality (CS = 0.75) and bridge strength centrality (CS = 0.67) exceeded the recommended threshold (> 0.5).

Conclusion

Academic stress and depressive symptoms form a dynamic interconnected network through social isolation (SS20) and low life satisfaction (CES-D16), suggesting the need to design targeted intervention strategies focusing on bridge nodes to mitigate their negative impact on middle school students.