谁最受益于夫妻关系教育:一种机器学习方法
Po-Heng Chen1, Hannah C Williamson1
1The University of Texas at Austin, Department of Human Development and Family Sciences.
Journal of consulting and clinical psychology
|January 15, 2026
概括
情侣关系教育 (CRE) 有利于心理困扰较高,关系幸福度较低的个人. 机器学习可以识别出最有可能改善关系质量和减少负面互动的情侣.
科学领域:
- 心理学 心理学 心理学
- 关系科学科学 关系科学
- 机器学习应用 机器学习应用
背景情况:
- 情侣关系教育 (CRE) 旨在改善关系功能,但显示出混合的结果.
- 干预的有效性可能取决于情侣的特点,需要更好的准.
- 以前的研究忽略了复杂的治疗前风险因素,导致不确定的发现.
研究的目的:
- 确定预处理特征,预测CRE中的差异性治疗效应.
- 在关系教育中利用机器学习进行个性化干预策略.
主要方法:
- 应用因果森林,一种机器学习技术,分析来自CRE两项随机对照试验的数据.
- 在6298对夫妇的数据上训练模型,并对1595对夫妇进行验证.
主要成果:
- 治疗前的心理压力和较低的关系幸福感预测了关系质量的更大改善.
- 高度的心理困扰和感知到的压力预测了负面关系情绪和行为的减少.
- 结果是可靠的,并在独立的数据集上得到验证.
结论:
- 治疗CRE的效果是异质的,因个体特征而异.
- 机器学习有效地识别出最有可能从CRE中受益的夫妇.
- 这种方法可以为更有针对性和有效的关系干预提供信息.
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