Yao Lijun, Xu Zhiwei, Zhao Xudong, Chen Yang, Liu Liang, Fu Xiaoming, Chen Fazhan
Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai 200124, PR China.
Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200434, PR China.
Heliyon. 2022 Nov 24;8(11):e11821. doi: 10.1016/j.heliyon.2022.e11821. eCollection 2022 Nov.
Side effects in the psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them.
We designed the psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients' side effects. A number of features were selected to distinguish the therapists by category. Six machine learning-based algorithms were selected and trained by our dataset to build classification models. We leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories.
Our study demonstrated the following: (1) Of the therapists, 316 perceived clients' side effects in psychotherapy, with a 59.6% incidence of side effects; the most common type was "make the clients or patients feel bad" (49.8%). (2) A Random Forest-based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients' side effects, with an F1 score of 0.722 and an AUC value of 0.717. (3) "Therapists' psychological activity" was the most relevant feature for distinguishing the therapist category.
Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their psychological states, was the most critical factor in predicting the therapist's perception of the side effects of psychotherapy.
心理治疗中的副作用有时不可避免。治疗师在心理治疗副作用中起着重要作用，但关于治疗师促成副作用产生的机制的定量研究很少。
我们设计了心理治疗副作用问卷-治疗师版（PSEQ-T），并通过微信公众号在线发布，530名治疗师参与了横断面分析。将治疗师分为察觉到和未察觉到来访者副作用的两组。选择了一些特征按类别区分治疗师。选择了六种基于机器学习的算法并通过我们的数据集进行训练以建立分类模型。我们利用夏普利值法（SHAP）来量化每个特征对治疗师类别的重要性。
我们的研究表明：（1）在治疗师中，316人察觉到心理治疗中来访者的副作用，副作用发生率为59.6%；最常见的类型是“使来访者或患者感觉变差”（49.8%）。（2）基于随机森林的机器学习分类器在区分察觉到和未察觉到来访者副作用的治疗师方面具有最佳预测性能，F1分数为0.722，AUC值为0.717。（- 3）“治疗师的心理活动”是区分治疗师类别的最相关特征。
我们的研究表明，治疗师对心理治疗技术和理论局限性的掌握，尤其是对其心理状态的认识和构建，是预测治疗师对心理治疗副作用感知的最关键因素。
