Race Matching in Predicting Relational Therapy Outcome: a Machine Learning Approach

IF 0.4 Q4 PSYCHOLOGY, CLINICAL International Journal of Systemic Therapy Pub Date : 2023-01-30 DOI:10.1080/2692398X.2023.2169028
Yi-Hsin Hung, Deanna Linville, Emily E. Janes, Simon Yee
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Abstract

ABSTRACT This study explores the relationship between therapist-client race/ethnicity matching on client treatment outcomes and whether other demographic factors contribute to treatment outcomes in a training clinic. An ANCOVA was conducted to examine the differences between race match and mismatch groups. A random forest algorithm was used to determine how racial matching conditions and other factors, such as gender, predict treatment outcomes. We found significant relationships between therapist-client race/ethnicity matching conditions and treatment outcomes for clients who received at least 10 sessions of therapy. However, results of the random forest algorithm indicated that race/ethnicity matching is one of the weakest predictors of treatment outcomes. Clinical implications and the limitations of the study are discussed.
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预测关系治疗结果的种族匹配:一种机器学习方法
摘要本研究探讨了治疗师-客户种族/民族匹配对客户治疗结果的影响,以及其他人口统计学因素是否有助于培训诊所的治疗结果。进行ANCOVA以检查种族匹配和不匹配组之间的差异。使用随机森林算法来确定种族匹配条件和其他因素(如性别)如何预测治疗结果。我们发现,治疗师-客户种族/民族匹配条件与接受至少10次治疗的客户的治疗结果之间存在显著关系。然而,随机森林算法的结果表明,种族/民族匹配是治疗结果的最弱预测因素之一。讨论了该研究的临床意义和局限性。
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CiteScore
1.10
自引率
20.00%
发文量
17
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