预测自我感知的自杀风险:贝叶斯的多层次分析自杀状态形式构造使用门诊评估
Jessica L Gerner1, Abby A Mandel1, Raymond P Tucker2,3
1Department of Psychology, The Catholic University of America, Washington, DC, USA.
The British journal of clinical psychology
|July 14, 2025
概括
自杀风险因素,如自我憎恨和激动,预测未来的自杀风险. 在自杀倾向协作评估 (CAMS) 框架内频繁监测这些因素可能会改善治疗结果.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 临床心理学 临床心理学
- 行为科学 行为科学
背景情况:
- 自杀倾向的协作评估 (CAMS) 是一个已建立的框架,用于管理自杀意念和行为.
- 自杀状况表 (SSF) 是CAMS的组成部分,用于监测自杀风险因素.
- 有限的研究存在于SSF核心评估变量的同时和未来的预测能力使用门诊评估.
研究的目的:
- 通过SSF核心评估测量关键自杀风险因素的短期变化.
- 检查这些风险因素对整体自我感知自杀风险的并发和潜在预测能力.
- 通过在学生群体中进行门诊评估来评估这些动态.
主要方法:
- 52名最近有自杀念头的本科生参与了这项研究.
- 参与者完成了10天的SSF核心评估变量的每日门诊评估.
- 使用多层模型分析风险因素与整体自杀风险之间的关系.
主要成果:
- 所有SSF核心评估变量都表现出显著的短期变化.
- 自我憎恨和绝望同时预测了整体自杀风险.
- 自我憎恨和动荡前性地预测了整体自杀风险,即使控制了基线自杀想法.
结论:
- 这项研究为SSF核心评估结构的短期可变性和预测实用性提供了新的见解.
- 这些风险因素在短时间内出现了相当大的波动,这表明频繁监测的价值.
- 在CAMS框架内加强监测可能是未来治疗研究和临床实践的关键要素.
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