预测青少年心理健康的多个领域的排名因素:贝叶斯的机器学习方法
Shan Zhao1, Xuanjing Li1, Xiang Gao1
1School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Child and adolescent psychiatry and mental health
|October 15, 2025
概括
生活经验和性显著影响青少年的心理健康,影响抑郁,焦虑和睡眠. 机器学习确定了中国青少年的关键风险和保护因素,突出了需要干预的领域.
科学领域:
- 青少年健康 青少年健康
- 心理健康研究 心理健康研究
- 机器学习在公共卫生中的应用
背景情况:
- 青少年心理健康问题的全球流行率不断上升,特别是在中国等发展中国家.
- 现有的研究往往忽视了风险/保护因素的结合以及先进的分析方法.
- 需要对青少年心理健康决定因素进行全面研究.
研究的目的:
- 确定和排名影响中国青少年心理健康结果的因素.
- 检查社会人口统计学,学术,课外,生活经验和弹性领域.
- 使用机器学习来预测抑郁症状,焦虑症状和睡眠质量.
主要方法:
- 在中国对3,526名青少年 (年龄11-15岁) 的横截面研究.
- 贝叶斯的机器学习方法用于因素识别和排名.
- 分析多个领域,包括生活经验和弹性.
主要成果:
- 机器学习模型显示出良好的预测能力 (伪R2 = 0.24-0.61).
- 生活压力,仁慈的事件,环境敏感性和转移和持续应对是所有结果的关键预测因素.
- 压力心态和表达性抑制独特地预测了睡眠质量和抑郁症状,分别.
结论:
- 生活经验和弹性因素是青少年心理健康的关键预测因素.
- 鉴定的因素为青少年的心理健康干预提供了目标.
- 需要进一步的研究来确定这些因素与心理健康之间的因果关系.
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