The evidence-based treatment (EBT) movement has primarily focused on core intervention content or treatment fidelity and has largely ignored practitioner skills to manage interpersonal process issues that emerge during treatment, especially with difficult-to-treat adolescents (delinquent, substance-using, medical non-adherence) and those of color. A chief complaint of “real world” practitioners about manualized treatments is the lack of correspondence between following a manual and managing microsocial interpersonal processes (e.g. negative affect) that arise in treating “real world clients.” Although family-based EBTs share core similarities (e.g. focus on family interactions, emphasis on practitioner engagement, family involvement), most of these treatments do not have an evidence base regarding common implementation and treatment process problems that practitioners experience in delivering particular models, especially in mid-treatment when demands on families to change their behavior is greatest in treatment – a lack that characterizes the field as a whole. Failure to effectively address common interpersonal processes with difficult-to-treat families likely undermines treatment fidelity and sustained use of EBTs, treatment outcome, and contributes to treatment dropout and treatment nonadherence. Recent advancements in wearables, sensing technologies, multivariate time-series analyses, and machine learning allow scientists to make significant advancements in the study of psychotherapy processes by looking “under the skin” of the provider–client interpersonal interactions that define therapeutic alliance, empathy, and empathic accuracy, along with the predictive validity of these therapy processes (therapeutic alliance, therapist empathy) to treatment outcome. Moreover, assessment of these processes can be extended to develop procedures for training providers to manage difficult interpersonal processes while maintaining a physiological profile that is consistent with astute skills in psychotherapeutic processes. This paper argues for opening the “black box” of therapy to advance the science of evidence-based psychotherapy by examining the clinical interior of evidence-based treatments to develop the next generation of audit- and feedback- (i.e., systemic review of professional performance) supervision systems.

循证治疗 （EBT） 运动主要关注核心干预内容或治疗保真度，在很大程度上忽视了从业者管理治疗过程中出现的人际关系过程问题的技能，尤其是对于难以治疗的青少年（犯罪、使用药物、医疗不依从）和有色人种。“现实世界”从业者对手动治疗的一个主要抱怨是，遵循手动和管理在治疗“现实世界客户”时出现的微观社会人际关系过程（例如负面影响）之间缺乏对应关系。尽管基于家庭的 EBT 具有核心相似之处（例如，关注家庭互动、强调从业者参与、家庭参与），但这些治疗中的大多数都没有关于从业者在提供特定模型时遇到的常见实施和治疗过程问题的证据基础，尤其是在治疗中期，当对家庭改变行为的要求在治疗中最大时——这是整个领域的特征。未能有效解决难以治疗家庭的常见人际关系过程可能会破坏治疗忠诚度和 EBT 的持续使用、治疗结果，并导致治疗退出和治疗不依从。 可穿戴设备、传感技术、多变量时间序列分析和机器学习方面的最新进展使科学家能够通过观察定义治疗联盟、同理心和移情准确性的提供者-客户人际互动的“皮下”，以及这些治疗过程（治疗联盟、治疗师同理心）对治疗结果的预测有效性，从而在心理治疗过程的研究方面取得重大进展。此外，对这些过程的评估可以扩展到为培训提供者制定程序，以管理困难的人际关系过程，同时保持与心理治疗过程中的精明技能一致的生理特征。本文主张打开治疗的“黑匣子”，通过检查循证治疗的临床内部来推进循证心理治疗的科学，以开发下一代审计和反馈（即对专业表现的系统审查）监督系统。