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

Vera Békés, Katie Aafjes-van Doorn, Sigal Zilcha-Mano, Tracy Prout, Leon Hoffman

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1403-1415
Number of pages13
JournalClinical Psychology and Psychotherapy
Volume28
Issue number6
DOIs
StatePublished - 1 Nov 2021

Bibliographical note

Funding Information:
The data collected in China were supported by a research grant by the China American Psychoanalytic Alliance (VB, KAVD). We thank Lauren Smith (Kenyon College) for her contributions to updating references and creating the figures in this manuscript.

Publisher Copyright:
© 2021 John Wiley & Sons, Ltd.

Keywords

  • COVID-19
  • UTAUT model
  • machine learning
  • online therapy
  • telepsychotherapy
  • therapists

ASJC Scopus subject areas

  • Clinical Psychology

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