Scholarlink

Background <p>Suicide is a major global public health concern, particularly among university students, who exhibit higher rates than their non-student peers. Although gender disparities in suicide rates are well documented, the underlying network structure of suicidal tendencies and comorbid psychological symptoms across genders remains insufficiently understood.</p> Methods <p>Psychometric network analysis was conducted to examine the interrelations between suicidal tendencies and core psychological symptoms among 1,021 vocational college students (488 males, 533 females). Gaussian Graphical Models were estimated using the EBICglasso method to identify central and bridge nodes, and Network Comparison Tests were used to evaluate gender differences in network structure, global metrics, and edge weights.</p> Results <p>Gender-specific networks revealed distinct patterns. Suicidal preparation emerged as a highly central symptom and stronger bridge node among females, whereas hopelessness was a key bridge factor among males. Global strength (<i>p</i> = 0.131) did not differ significantly between genders, whereas global expected influence (<i>p</i> = 0.007) showed significant gender differences. Several nodes and edges also demonstrated gender-specific patterns.</p> Conclusions <p>Depression emerged as a pivotal core symptom, and suicidal preparation acted as a crucial bridge linking psychological symptoms to suicide risk among vocational college students. Significant gender differences were observed in symptom centrality, bridge functions, and specific symptom–symptom interactions. Females appeared more vulnerable through pathways involving suicidal preparation, whereas males were more affected by hopelessness. These findings underscore the need for gender-tailored suicide prevention strategies targeting these distinct network mechanisms.</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

Network analysis of suicidal tendency and mental health symptoms in college students: a gender-differences comparison

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月1日

查看全文
  • Mingfa Wu,
  • Hui Wang,
  • Ming Li,
  • Zhenyang Gao,
  • Qingjing Lei,
  • Yiwen Yan,
  • Dorje Drolma,
  • Jingyu Yang,
  • Zhongli Lan,
  • Lijuan Liang

摘要

Background

Suicide is a major global public health concern, particularly among university students, who exhibit higher rates than their non-student peers. Although gender disparities in suicide rates are well documented, the underlying network structure of suicidal tendencies and comorbid psychological symptoms across genders remains insufficiently understood.

Methods

Psychometric network analysis was conducted to examine the interrelations between suicidal tendencies and core psychological symptoms among 1,021 vocational college students (488 males, 533 females). Gaussian Graphical Models were estimated using the EBICglasso method to identify central and bridge nodes, and Network Comparison Tests were used to evaluate gender differences in network structure, global metrics, and edge weights.

Results

Gender-specific networks revealed distinct patterns. Suicidal preparation emerged as a highly central symptom and stronger bridge node among females, whereas hopelessness was a key bridge factor among males. Global strength (p = 0.131) did not differ significantly between genders, whereas global expected influence (p = 0.007) showed significant gender differences. Several nodes and edges also demonstrated gender-specific patterns.

Conclusions

Depression emerged as a pivotal core symptom, and suicidal preparation acted as a crucial bridge linking psychological symptoms to suicide risk among vocational college students. Significant gender differences were observed in symptom centrality, bridge functions, and specific symptom–symptom interactions. Females appeared more vulnerable through pathways involving suicidal preparation, whereas males were more affected by hopelessness. These findings underscore the need for gender-tailored suicide prevention strategies targeting these distinct network mechanisms.