匹配:客户和治疗师通过机器学习进行匹配
Seth M Peacock1, Ian H Goodin1, Ryan K Wood1
1Brigham Young University, Provo, UT, USA.
概括
一个新的框架使用档案数据和计算机科学来将客户与治疗师相匹配,改善咨询结果. 这种数据驱动的方法提高了客户的成功,同时尊重治疗师的可用性.
科学领域:
- 临床心理学 临床心理学
- 医疗信息学 医疗信息学
- 计算机科学 计算机科学
背景情况:
- 治疗师的分配显著影响咨询的成功.
- 当前的方法通常依赖于简单的可用性,可能缺少最佳匹配.
研究的目的:
- 利用档案数据和算法,开发和评估一个用于将客户与治疗师匹配的框架.
- 与分配给下一个可用的治疗师相比,改善客户的结果.
- 将治疗师可用性限制纳入匹配过程中.
主要方法:
- 利用来自10100个治疗课程 (2014-2019) 的高维度调查数据 (CCAPS-62,OQ-45).
- 使用预测模型估计的治疗师-客户结果关联.
- 实施了尊重治疗师可用性的受约束匹配算法.
- 通过模拟和统计测试来评估性能.
主要成果:
- 该模型预测了与0.145-3.65OQ-45点 (d=0.11) 的平均改善相关的治疗师-客户对.
- 这种改善在统计学上是显著的 (95%置信区间).
- 该方法显示了改善结果的潜力,对等待时间和行政变更的影响最小.
结论:
- 数据驱动的客户-治疗师匹配可以显著改善咨询结果.
- 考虑治疗师可用性限制对于实际实施至关重要.
- 这种方法提供了一个可扩展的解决方案,以提高心理健康服务的有效性.
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