Abstract

Objective: To better understand the complexity of dyadic processes, such as the mechanisms of the working alliance, researchers recommend taking advantage of innovations in data analytic procedures when studying the interactions between therapists and patients that are associated with favorable therapeutic outcomes. Inspired by a recent line of alliance research using dyadic multilevel modeling, the present study investigated the hypothesis that convergence in the patient-therapist working alliance (i.e., increased similarity in ratings of the alliance across treatment) would be associated with better outcomes. Method: Data were retrieved from two samples: 1. A randomized controlled trial for treatment resistant depression (N = 96 dyads), and 2. An archival dataset of naturalistic psychotherapies from public health care (N = 139 dyads). Multilevel growth curve analysis was employed to investigate the degree of change in session-to-session agreement of global WAI ratings between therapists and patients (i.e., alliance convergence) as a predictor of symptom reduction in the BDI-II and the SCL-90R. Results: Contrary to our expectations, alliance convergence did not predict outcome in either sample, but was negatively associated with symptom severity in Study 2. Implications for understanding the complexity of dyadic processes and alliance work in psychotherapy are discussed.

摘要

目的：为了更好地了解二进位过程的复杂性，例如工作联盟的机制，研究人员建议在研究治疗师与患者之间的相互作用并获得良好治疗效果时，应利用数据分析程序的创新。受最近使用二元多级建模的联盟研究的启发，本研究调查了以下假设：患者-治疗师工作联盟的趋同（即，各治疗之间联盟评级的相似性提高）将带来更好的结果。方法：从两个样本中检索数据：1.治疗性抑郁症的随机对照试验（N = 96 dyads）；以及2.来自公共卫生保健的自然主义心理疗​​法的档案数据集（N  = 139 dyads）。采用多级生长曲线分析来调查治疗师和患者之间全球WAI评分的会话间一致性变化程度（即联盟趋同），作为BDI-II和SCL-90R症状减轻的预测指标。结果：与我们的预期相反，联盟收敛不能预测两个样本的结果，但与研究2中的症状严重程度呈负相关。讨论了理解心理疗法中二元过程和联盟工作的复杂性的含义。