第一印象是重要的:治疗师对患者的动机和帮助联盟的印象预测了心理治疗中断
Kristin Jankowsky1, Johannes Zimmermann1, Ulrich Jaeger2
1Department of Psychology, University of Kassel, Kassel, Germany.
概括
心理治疗中断是一个常见的问题,可以使用机器学习模型来预测. 患者的动机和治疗联盟是降低学率的关键预测因素.
科学领域:
- 心理学 心理学 心理学
- 精神病学是一个精神病学.
- 医疗保健服务研究 医疗服务研究
背景情况:
- 心理治疗中断影响个人,临床医生和医疗保健系统,在元分析上估计的比例约为25%.
- 准确地预测脱学率对于制定有效的干预措施来应对这一问题至关重要.
研究的目的:
- 为了比较后勤回归模型与机器学习算法 (弹性净回归和梯度增强机器) 的预测性能,用于治疗中断.
- 通过基线和初始过程变量,确定住院患者治疗中断的关键预测因素.
主要方法:
- 使用了两个大型住院患者样本 (N=1,691和N=12,473).
- 使用后勤回归,弹性净回归和梯度增强机器来预测治疗中断.
- 包括患者和治疗师报告的基线和初始过程变量.
主要成果:
- 与后勤回归相比,机器学习算法显示出类似的和更高的预测准确度 (AUC .73和 .83).
- 确定最重要的预测因素是患者的初始动机和治疗师评价的治疗联盟.
- 通过规范化的特征选择提高了预测性能,而非线性或相互作用效应并不重要.
结论:
- 在自然主义的住院患者环境中,使用基线指标和初始治疗师印象可以预测治疗中断.
- 针对患者动机和治疗联盟的干预措施显示出降低学率的希望.
- 规范化技术提高了治疗中断的预测准确性.
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