Zhang Xinyao, Goldberg Simon B, Baldwin Scott A, Tanana Michael J, Weitzman Lauren M, Narayanan Shrikanth S, Atkins David C, Imel Zac E
Department of Educational Psychology, University of Utah.
Department of Counseling Psychology, University of Wisconsin-Madison.
J Consult Clin Psychol. 2025 Feb;93(2):110-119. doi: 10.1037/ccp0000935.
This study applied a machine-learning-based skill assessment system to investigate the association between supportive counseling skills (empathy, open questions, and reflections) and treatment outcomes. We hypothesized that higher empathy and higher use of open questions and reflections would be associated with greater symptom reduction.
We used a data set with 2,974 sessions, 610 clients, and 48 therapists collected from a university counseling center, which included 845,953 rated therapist statements. Client outcome was routinely monitored by the Counseling Center Assessment of Psychological Symptoms Instruments. Therapists' skills were measured via computer by a bidirectional-long-short-term-memory-based system that rated use of supportive counseling skills. We used multilevel modeling to separate the between-therapist and the within-therapist associations of the skills and outcome.
Use of open questions and reflections was associated with client symptom reduction between therapists but not within therapists. We did not find significant associations between therapist empathy and client symptom reduction but found that empathy was negatively associated with clients' baseline symptom level within therapists.
Therapist exploration of clients' experience and expression of understanding may be important skills that are associated with clients' better outcomes. This study highlights the importance of support counseling skills, as well as the potential of machine-learning-based measures in psychotherapy research. We discuss the limitations of the study, including the limitations related to the speaker recognition system and potential reasons for the lack of association between empathy and client outcome. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
本研究应用基于机器学习的技能评估系统，以探究支持性咨询技能（同理心、开放式问题和反馈）与治疗效果之间的关联。我们假设，更高的同理心以及更多地使用开放式问题和反馈与更大程度的症状减轻相关。
我们使用了一个数据集，该数据集包含从一所大学咨询中心收集的2974次咨询会话、610名客户和48名治疗师的信息，其中包括845953条经过评级的治疗师陈述。客户的治疗效果通过咨询中心心理症状评估工具进行常规监测。治疗师的技能通过一个基于双向长短期记忆的系统进行计算机测量，该系统对支持性咨询技能的使用进行评级。我们使用多层模型来区分治疗师之间以及治疗师内部技能与治疗效果的关联。
在治疗师之间，使用开放式问题和反馈与客户症状减轻相关，但在治疗师内部则不然。我们没有发现治疗师的同理心与客户症状减轻之间存在显著关联，但发现同理心与治疗师内部客户的基线症状水平呈负相关。
治疗师对客户经历的探索和理解的表达可能是与客户更好治疗效果相关的重要技能。本研究强调了支持性咨询技能的重要性，以及基于机器学习的测量方法在心理治疗研究中的潜力。我们讨论了该研究的局限性，包括与说话者识别系统相关的局限性以及同理心与客户治疗效果缺乏关联的潜在原因。（《心理学文摘数据库记录》（c）2025美国心理学会，保留所有权利）
