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

被引：3

作者：

Hung, Yi-Hsin ^{[1
]}

Linville, Deanna ^{[2
]}

Janes, Emily ^{[1
]}

Yee, Simon ^{[3
]}

机构：

[1] Texas Tech Univ, Lubbock, TX 79409 USA

[2] Univ Oregon, Ctr Equ Promot, Ctr Transformat Healing, Eugene, OR USA

[3] Georgia Inst Technol, Atlanta, GA USA

来源：

INTERNATIONAL JOURNAL OF SYSTEMIC THERAPY | 2023年 / 34卷 / 02期

关键词：

Race matching; treatment outcome; relational therapy; random forest; ALLIANCE; CLIENTS; GENDER;

D O I：

10.1080/2692398X.2023.2169028

中图分类号：

B849 [应用心理学];

学科分类号：

040203 ;

摘要：

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.

引用

页码：83 / 94

页数：12

共 19 条

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