Yao Lijun, Wang Ziyi, Gu Hong, Zhao Xudong, Chen Yang, Liu Liang
Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Front Psychiatry. 2023 Jan 19;14:947081. doi: 10.3389/fpsyt.2023.947081. eCollection 2023.
Effective psychotherapy should satisfy the client, but that satisfaction depends on many factors. We do not fully understand the factors that affect client satisfaction with psychotherapy and how these factors synergistically affect a client's psychotherapy experience.
This study aims to use machine learning to predict Chinese clients' satisfaction with psychotherapy and analyze potential outcome contributors.
In this cross-sectional investigation, a self-compiled online questionnaire was delivered through the WeChat app. The information of 791 participants who had received psychotherapy was used in the study. A series of features, for example, the participants' demographic features and psychotherapy-related features, were chosen to distinguish between participants satisfied and dissatisfied with the psychotherapy they received. With our dataset, we trained seven supervised machine-learning-based algorithms to implement prediction models.
Among the 791 participants, 619 (78.3%) reported being satisfied with the psychotherapy sessions that they received. The occupation of the clients, the location of psychotherapy, and the form of access to psychotherapy are the three most recognizable features that determined whether clients are satisfied with psychotherapy. The machine-learning model based on the CatBoost achieved the highest prediction performance in classifying satisfied and psychotherapy clients with an F1 score of 0.758.
This study clarified the factors related to clients' satisfaction with psychotherapy, and the machine-learning-based classifier accurately distinguished clients who were satisfied or unsatisfied with psychotherapy. These results will help provide better psychotherapy strategies for specific clients, so they may achieve better therapeutic outcomes.
有效的心理治疗应使来访者满意，但这种满意度取决于多种因素。我们尚未完全理解影响来访者对心理治疗满意度的因素，以及这些因素如何协同影响来访者的心理治疗体验。
本研究旨在使用机器学习预测中国来访者对心理治疗的满意度，并分析潜在的结果影响因素。
在这项横断面调查中，通过微信应用程序发放了一份自编的在线问卷。研究使用了791名接受过心理治疗的参与者的信息。选择了一系列特征，例如参与者的人口统计学特征和与心理治疗相关的特征，以区分对所接受心理治疗满意和不满意的参与者。利用我们的数据集，我们训练了七种基于监督机器学习的算法来实现预测模型。
在791名参与者中，619名（78.3%）报告对他们接受的心理治疗感到满意。来访者的职业、心理治疗地点和获得心理治疗的形式是决定来访者是否对心理治疗满意的三个最显著特征。基于CatBoost的机器学习模型在区分满意和不满意心理治疗的来访者方面取得了最高的预测性能，F1分数为0.758。
本研究阐明了与来访者对心理治疗满意度相关的因素，基于机器学习的分类器准确地区分了对心理治疗满意或不满意的来访者。这些结果将有助于为特定来访者提供更好的心理治疗策略，从而可能实现更好的治疗效果。
