University of Trier.
University of Trier.
Behav Ther. 2021 Nov;52(6):1489-1501. doi: 10.1016/j.beth.2021.05.001. Epub 2021 May 11.
The current study employed machine learning to investigate whether the inclusion of observer-rated therapist interventions and skills in early sessions of psychotherapy improved dropout prediction beyond intake assessments. Patients were treated by postgraduate clinicians at a university outpatient clinic. Psychometric instruments were assessed at intake and therapeutic interventions and skills in the third session were routinely rated by independent observers. After variable preselection, an elastic net algorithm was used to build two dropout prediction models, one including and one excluding observer-rated session variables. The best model included observer-rated variables and was significantly superior to the model including intake variables only. Alongside intake variables, two observer-rated variables significantly predicted dropout: therapist use of feedback and summaries and treatment difficulty. Although not retained in the final prediction model, the observer-rated use of cognitive techniques was also significantly correlated with dropout. Observer ratings of therapist interventions and skills in early sessions of psychotherapy improve predictors of dropout from psychotherapy beyond intake variables alone. Future research could work toward personalizing dropout predictions to the specific dyad, thereby improving their validity and aiding therapists to recognize and react to increased dropout risk.
本研究采用机器学习方法探讨了在心理治疗的早期阶段纳入观察者评估的治疗师干预和技能是否可以提高除了摄入评估之外的辍学预测。患者由大学门诊诊所的研究生临床医生治疗。在摄入时评估心理测量工具，并且由独立观察者定期评估第三个疗程中的治疗干预和技能。在进行变量预选之后，使用弹性网算法构建了两个辍学预测模型，一个包括观察者评估的会话变量，另一个不包括。最好的模型包括观察者评估的变量，并且明显优于仅包括摄入变量的模型。除了摄入变量外，两个观察者评估的变量也显著预测了辍学：治疗师使用反馈和总结以及治疗难度。尽管未保留在最终的预测模型中，但观察者评估的认知技术的使用也与辍学显著相关。在心理治疗的早期阶段，对治疗师干预和技能的观察者评估可以提高除了摄入变量之外的辍学预测因素。未来的研究可以努力将辍学预测个性化到特定的医患关系，从而提高其有效性，并帮助治疗师识别和应对增加的辍学风险。
