A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity
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| Format: | Artículo científico |
| Langue: | en |
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Colegio Oficial de Psicólogos de Madrid
2021
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| _version_ | 1876466505921593344 |
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| author | Asghar Ahmadi |
| author_facet | Asghar Ahmadi |
| contents | A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity Asghar Ahmadi Michael Noetel Melissa Schellekens Philip Parker Devan Antczak Mark Beauchamp Theresa Dicke Carmel Diezmann Anthony Maeder Nikos Ntoumanis Alexander Yeung Chris Lonsdale Psicología Feedback Machine learning Treatment fidelity Treatment integrity Clinical supervision 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 generalisability is unclear. We collated and critiqued all implementations of machine learning to assess the verbal behaviour of all helping professionals, with particular emphasis on treatment fidelity for therapists. We conducted searches using nine electronic databases for automated approaches of coding verbal behaviour in therapy and similar contexts. We completed screening, extraction, and quality assessment in duplicate. Fifty-two studies met our inclusion criteria (65.3% in psychotherapy). Automated coding methods performed better than chance, and some methods showed near human-level performance; performance tended to be better with larger data sets, a smaller number of codes, conceptually simple codes, and when predicting session-level ratings than utterance-level ones. Few studies adhered to best-practice machine learning guidelines. Machine learning demonstrated promising results, particularly where there are large, annotated datasets and a modest number of concrete features to code. These methods are novel, cost-effective, scalable ways of assessing fidelity and providing therapists with individualised, prompt, and objective feedback. 2021 artículo científico 1132-0559 https://www.redalyc.org/articulo.oa?id=179869496003 https://www.redalyc.org/journal/1798/179869496003/ https://www.redalyc.org/journal/1798/179869496003/html/ https://www.redalyc.org/journal/1798/179869496003/179869496003.epub https://www.redalyc.org/journal/1798/179869496003/movil 10.7440/res64.2018.03 en http://www.redalyc.org/revista.oa?id=1798 Psychosocial Intervention application/pdf Colegio Oficial de Psicólogos de Madrid Psychosocial Intervention (España) Num.3 Vol.30 |
| format | Artículo científico |
| id | redalyc_179869496003 |
| institution | Redalyc |
| language | en |
| publishDate | 2021 |
| publisher | Colegio Oficial de Psicólogos de Madrid |
| spellingShingle | A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity Asghar Ahmadi Psicología Feedback Machine learning Treatment fidelity Treatment integrity Clinical supervision A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity Asghar Ahmadi Michael Noetel Melissa Schellekens Philip Parker Devan Antczak Mark Beauchamp Theresa Dicke Carmel Diezmann Anthony Maeder Nikos Ntoumanis Alexander Yeung Chris Lonsdale Psicología Feedback Machine learning Treatment fidelity Treatment integrity Clinical supervision 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 generalisability is unclear. We collated and critiqued all implementations of machine learning to assess the verbal behaviour of all helping professionals, with particular emphasis on treatment fidelity for therapists. We conducted searches using nine electronic databases for automated approaches of coding verbal behaviour in therapy and similar contexts. We completed screening, extraction, and quality assessment in duplicate. Fifty-two studies met our inclusion criteria (65.3% in psychotherapy). Automated coding methods performed better than chance, and some methods showed near human-level performance; performance tended to be better with larger data sets, a smaller number of codes, conceptually simple codes, and when predicting session-level ratings than utterance-level ones. Few studies adhered to best-practice machine learning guidelines. Machine learning demonstrated promising results, particularly where there are large, annotated datasets and a modest number of concrete features to code. These methods are novel, cost-effective, scalable ways of assessing fidelity and providing therapists with individualised, prompt, and objective feedback. 2021 artículo científico 1132-0559 https://www.redalyc.org/articulo.oa?id=179869496003 https://www.redalyc.org/journal/1798/179869496003/ https://www.redalyc.org/journal/1798/179869496003/html/ https://www.redalyc.org/journal/1798/179869496003/179869496003.epub https://www.redalyc.org/journal/1798/179869496003/movil 10.7440/res64.2018.03 en http://www.redalyc.org/revista.oa?id=1798 Psychosocial Intervention application/pdf Colegio Oficial de Psicólogos de Madrid Psychosocial Intervention (España) Num.3 Vol.30 |
| title | A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity |
| topic | Psicología Feedback Machine learning Treatment fidelity Treatment integrity Clinical supervision |
| url | https://www.redalyc.org/articulo.oa?id=179869496003 https://www.redalyc.org/journal/1798/179869496003/ https://www.redalyc.org/journal/1798/179869496003/html/ https://www.redalyc.org/journal/1798/179869496003/179869496003.epub https://www.redalyc.org/journal/1798/179869496003/movil |