Abstract

Objective

To identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students.

Methods

356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores.

Results

9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95%CI: 28–28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk.

Conclusion

Analyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.

中文翻译：

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大学生自杀想法和行为的风险及保护因素的机器学习分析

摘要

客观的

确定与大学生自杀想法和行为 (STB) 相关的稳健且可重复的因素。

方法

356 名一年级大学生在大学第一学期和年底完成了大量人口统计和临床相关的自我报告测量（n  = 228）。自杀行为问卷修订版 (SBQ-R) 评估了 STB。使用堆叠和嵌套交叉验证的机器学习 (ML) 管道检查了 SBQ-R 分数的相关性。

结果

SBQ-R 发现 9.6% 的学生面临重大 STB 风险。ML 算法解释了基线 SBQ-R 评分中 28.3% 的方差（95%CI：28-28.5%），其中抑郁严重程度、社会孤立、生活意义和目的以及积极影响是最重要的因素。到年底，STB 显着减少，只有 1.8% 的学生被确定存在重大风险。

结论

分析复制了大学第一学期与 STB 相关的已知因素，并确定了新颖的、潜在可改变的因素，包括积极影响和社会联系。