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Therapists and psychotherapy side effects in China: A machine learning-based study
OBJECTIVE: Side effects in the psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. METHODS: We designed the psychotherapy Side Effects...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9706699/ https://www.ncbi.nlm.nih.gov/pubmed/36458310 http://dx.doi.org/10.1016/j.heliyon.2022.e11821 |
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author | Yao, Lijun Xu, Zhiwei Zhao, Xudong Chen, Yang Liu, Liang Fu, Xiaoming Chen, Fazhan |
author_facet | Yao, Lijun Xu, Zhiwei Zhao, Xudong Chen, Yang Liu, Liang Fu, Xiaoming Chen, Fazhan |
author_sort | Yao, Lijun |
collection | PubMed |
description | OBJECTIVE: Side effects in the psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. METHODS: We designed the psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning-based algorithms were selected and trained by our dataset to build classification models. We leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories. RESULTS: Our study demonstrated the following: (1) Of the therapists, 316 perceived clients’ side effects in psychotherapy, with a 59.6% incidence of side effects; the most common type was “make the clients or patients feel bad” (49.8%). (2) A Random Forest-based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients' side effects, with an F1 score of 0.722 and an AUC value of 0.717. (3) “Therapists’ psychological activity” was the most relevant feature for distinguishing the therapist category. CONCLUSIONS: Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their psychological states, was the most critical factor in predicting the therapist's perception of the side effects of psychotherapy. |
format | Online Article Text |
id | pubmed-9706699 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97066992022-11-30 Therapists and psychotherapy side effects in China: A machine learning-based study Yao, Lijun Xu, Zhiwei Zhao, Xudong Chen, Yang Liu, Liang Fu, Xiaoming Chen, Fazhan Heliyon Research Article OBJECTIVE: Side effects in the psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. METHODS: We designed the psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning-based algorithms were selected and trained by our dataset to build classification models. We leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories. RESULTS: Our study demonstrated the following: (1) Of the therapists, 316 perceived clients’ side effects in psychotherapy, with a 59.6% incidence of side effects; the most common type was “make the clients or patients feel bad” (49.8%). (2) A Random Forest-based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients' side effects, with an F1 score of 0.722 and an AUC value of 0.717. (3) “Therapists’ psychological activity” was the most relevant feature for distinguishing the therapist category. CONCLUSIONS: Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their psychological states, was the most critical factor in predicting the therapist's perception of the side effects of psychotherapy. Elsevier 2022-11-24 /pmc/articles/PMC9706699/ /pubmed/36458310 http://dx.doi.org/10.1016/j.heliyon.2022.e11821 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Yao, Lijun Xu, Zhiwei Zhao, Xudong Chen, Yang Liu, Liang Fu, Xiaoming Chen, Fazhan Therapists and psychotherapy side effects in China: A machine learning-based study |
title | Therapists and psychotherapy side effects in China: A machine learning-based study |
title_full | Therapists and psychotherapy side effects in China: A machine learning-based study |
title_fullStr | Therapists and psychotherapy side effects in China: A machine learning-based study |
title_full_unstemmed | Therapists and psychotherapy side effects in China: A machine learning-based study |
title_short | Therapists and psychotherapy side effects in China: A machine learning-based study |
title_sort | therapists and psychotherapy side effects in china: a machine learning-based study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9706699/ https://www.ncbi.nlm.nih.gov/pubmed/36458310 http://dx.doi.org/10.1016/j.heliyon.2022.e11821 |
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