在16至25岁的年轻人中使用机器学习识别分离的初步风险概况
Roberta McGuinness1, Daniel Herring2, Xinyi Wu2
1School of Psychology, University of Birmingham, Birmingham, UK.
Early intervention in psychiatry
|February 10, 2025
概括
日常压力,童年创伤,孤独和边缘化显著增加了年轻成年人的分离风险. 机器学习根据年龄确定了不同的风险概况,突出了导致分离体验的因素的复杂相互作用.
科学领域:
- 心理学 心理学 心理学
- 临床心理学 临床心理学
- 发展心理学 发展心理学
背景情况:
- 分离与严重的心理健康问题有关,包括自杀和自我伤害.
- 青少年和年轻人不成比例地受到分离体验的影响.
- 缺乏对分离风险增加的全面的多因素解释.
研究的目的:
- 调查五种风险因素对分离的相对影响.
- 使用机器学习开发"感觉异常"解离 (FSA-解离) 的高风险概况.
- 了解导致年轻成年人解离的因素的相互作用.
主要方法:
- 截面在线调查数据来自英国2384名年龄在16-25岁之间的个人.
- 多重回归分析用于评估童年创伤,孤独,边缘化,社会经济地位和日常压力的影响.
- 机器学习用于探索性高风险概况生成.
主要成果:
- 日常压力,童年创伤,孤独和边缘化是FSA分离的重要预测因素.
- 机器学习揭示了复杂的相互作用:年轻人 (16-20) 显示与负面自我概念和抑郁症相关的风险,而老年人 (21-25) 受焦虑和情绪调节困难的影响.
- 边缘化和童年创伤在各个年龄组中始终很重要.
结论:
- 研究结果强调了年轻人分离的多因素性质.
- 结果可以为临床评估和有针对性的预防策略提供信息.
- 提高在主流服务中对分离的认识至关重要.
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