Psychotherapists' acceptance of telepsychotherapy during the COVID-19 pandemic: A machine learning approach

Objective: This study aimed to develop predictive models of three aspects of psychotherapists' acceptance of telepsychotherapy (TPT) during the COVID-19 pandemic, attitudes towards TPT technology, concerns about using TPT technology and intention to use TPT technology in the future. Method: Therapists (n = 795) responded to a survey about their TPT experiences during the pandemic, including quality of the therapeutic relationship, professional self-doubt, vicarious trauma and TPT acceptance. Regression decision tree machine learning analyses were used to build prediction models for each of three aspects of TPT acceptance in a training subset of the data and subsequently tested in the remaining subset of the total sample. Results: Attitudes towards TPT were most positive for therapists who reported a neutral or strong online working alliance with their patients, especially if they experienced little professional self-doubt and were younger than 40 years old. Therapists who were most concerned about TPT were those who reported higher levels of professional self-doubt, particularly if they also reported vicarious trauma experiences. Therapists who reported low working alliance with their patients were least likely to use TPT in the future. Performance metrics for the decision trees indicated that these three models held up well in an out-of-sample dataset. Conclusions: Therapists' professional self-doubt and the quality of their working alliance with their online patients appear to be the most pertinent factors associated with therapists' acceptance of TPT technology during COVID-19 and should be addressed in future training and research.