A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity

A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity

Many psychological treatments have been shown to be cost-effective and efficacious, as long as they are implemented faithfully. Assessing fidelity and providing feedback is expensive and time-consuming. Machine learning has been used to assess treatment fidelity, but the reliability and generalisabi...

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Bibliographic Details
Main Authors: Asghar Ahmadi, Michael Noetel, Melissa Schellekens, Philip Parker, Devan Antczak, Mark Beauchamp, Theresa Dicke, Carmel Diezmann, Anthony Maeder, Nikos Ntoumanis, Alexander Yeung, Chris Lonsdale
Format: Article
Language:English
Published: Colegio Oficial de Psicólogos de Madrid 2021-07-01
Series:Psychosocial Intervention
Subjects:
machine learning
treatment fidelity
treatment integrity
clinical supervision
feedback
Online Access: https://journals.copmadrid.org/pi/art/pi2021a4

Internet

https://journals.copmadrid.org/pi/art/pi2021a4

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