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 Thera...
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Published in | Clinical psychology and psychotherapy Vol. 28; no. 6; pp. 1403 - 1415 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
England
John Wiley and Sons, Limited
01.11.2021
John Wiley and Sons Inc |
Subjects | |
Online Access | Get full text |
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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. |
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AbstractList | 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. 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. ObjectiveThis 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.MethodTherapists (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.ResultsAttitudes 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.ConclusionsTherapists' 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. 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.OBJECTIVEThis 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.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.METHODTherapists (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.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.RESULTSAttitudes 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.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.CONCLUSIONSTherapists' 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. 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. 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. 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. 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. |
Author | Zilcha‐Mano, Sigal Aafjes‐van Doorn, Katie Prout, Tracy Hoffman, Leon Békés, Vera |
AuthorAffiliation | 1 Ferkauf Graduate School of Psychology Yeshiva University New York New York USA 2 Department of Psychology University of Haifa Haifa Israel 3 New York Psychoanalytic Institute New York New York USA |
AuthorAffiliation_xml | – name: 2 Department of Psychology University of Haifa Haifa Israel – name: 1 Ferkauf Graduate School of Psychology Yeshiva University New York New York USA – name: 3 New York Psychoanalytic Institute New York New York USA |
Author_xml | – sequence: 1 givenname: Vera orcidid: 0000-0003-3043-5155 surname: Békés fullname: Békés, Vera email: vera.bekes@yu.edu organization: Yeshiva University – sequence: 2 givenname: Katie orcidid: 0000-0003-2584-5897 surname: Aafjes‐van Doorn fullname: Aafjes‐van Doorn, Katie organization: Yeshiva University – sequence: 3 givenname: Sigal orcidid: 0000-0002-5645-4429 surname: Zilcha‐Mano fullname: Zilcha‐Mano, Sigal organization: University of Haifa – sequence: 4 givenname: Tracy orcidid: 0000-0002-3650-5890 surname: Prout fullname: Prout, Tracy organization: Yeshiva University – sequence: 5 givenname: Leon orcidid: 0000-0001-7466-5513 surname: Hoffman fullname: Hoffman, Leon organization: New York Psychoanalytic Institute |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34723404$$D View this record in MEDLINE/PubMed |
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Keywords | COVID-19 therapists machine learning online therapy UTAUT model telepsychotherapy |
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License | 2021 John Wiley & Sons, Ltd. This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency. |
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Notes | Funding information Vera Békés and Katie Aafjes‐van Doorn shared first authorship. China American Psychoanalytic Alliance ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Funding information China American Psychoanalytic Alliance |
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This study aimed to develop predictive models of three aspects of psychotherapists' acceptance of telepsychotherapy (TPT) during the COVID‐19... This study aimed to develop predictive models of three aspects of psychotherapists' acceptance of telepsychotherapy (TPT) during the COVID-19 pandemic,... Abstract Objective This study aimed to develop predictive models of three aspects of psychotherapists' acceptance of telepsychotherapy (TPT) during the... ObjectiveThis study aimed to develop predictive models of three aspects of psychotherapists' acceptance of telepsychotherapy (TPT) during the COVID‐19... |
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SubjectTerms | Adult COVID-19 Humans Learning algorithms Machine Learning online therapy Pandemics Prediction models Psychotherapists Psychotherapy SARS-CoV-2 Special Issue Telemedicine telepsychotherapy Therapists Training Trauma UTAUT model |
Title | Psychotherapists' acceptance of telepsychotherapy during the COVID‐19 pandemic: A machine learning approach |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fcpp.2682 https://www.ncbi.nlm.nih.gov/pubmed/34723404 https://www.proquest.com/docview/2618220593 https://www.proquest.com/docview/2591216970 https://pubmed.ncbi.nlm.nih.gov/PMC8652775 |
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