Ryan Kilcullen J, Castonguay Louis G, Janis Rebecca A, Hallquist Michael N, Hayes Jeffrey A, Locke Benjamin D
Department of Psychology, Pennsylvania State University, University Park, PA, USA.
Department of Educational Psychology, Counseling, and Special Education, Pennsylvania State University, University Park, PA, USA.
Psychother Res. 2021 Jan;31(1):63-77. doi: 10.1080/10503307.2020.1762948. Epub 2020 May 14.
There is a paucity of studies examining the experience of clients who undergo multiple courses of psychotherapy. Conducted within a large practice research network, this study demonstrated that returning therapy clients comprise a considerable portion of the clinical population in university counseling settings, and identified variables associated with return to therapy. Utilizing data spanning 2013 to 2017, statistical variable selection for predicting return to therapy was conducted via grouped least absolute shrinkage and selection operator (grouped LASSO) applied to logistic regression. The grouped LASSO approach is described in detail to facilitate learning and replication. The paper also addresses methodological considerations related to this approach, such as sample size, generalizability, as well as general strengths and limitations. Attendance rate, duration of initial treatment course, social anxiety, perceived social support, academic distress, and alcohol use were identified as predictive of return to therapy. Findings could help inform more cost-effective policies for session limits (e.g., extending session limits for clients with social anxiety), referral decisions (e.g., for clients with alcohol use problems), and appointment reminders (based on the association between poor attendance rate and return to therapy). Taking into account the many reasons that can explain why clients do or do not return to therapy, these findings also could inform clinicians' early case conceptualizations and treatment interventions.
关于接受多疗程心理治疗的来访者体验的研究较少。本研究在一个大型实践研究网络中开展，结果表明，复诊的心理治疗来访者在大学咨询机构的临床人群中占相当大的比例，并确定了与复诊相关的变量。利用2013年至2017年的数据，通过应用于逻辑回归的分组最小绝对收缩和选择算子（分组套索法）对预测复诊的统计变量进行了选择。详细描述了分组套索法，以促进学习和重复研究。本文还讨论了与该方法相关的方法学考量，如样本量、可推广性以及总体优势和局限性。出勤率、初始治疗疗程时长、社交焦虑、感知到的社会支持、学业困扰和饮酒被确定为复诊的预测因素。研究结果有助于为更具成本效益的疗程限制政策（如延长社交焦虑来访者的疗程限制）、转诊决策（如针对有饮酒问题的来访者）和预约提醒（基于低出勤率与复诊之间的关联）提供参考。考虑到可以解释来访者是否复诊的诸多原因，这些研究结果还可为临床医生早期的病例概念化和治疗干预提供参考。
