Data mining techniques in psychotherapy: applications for studying therapeutic alliance
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/90409 |
Resumo: | Data has been uploaded to the public repository; Kaggle. @misc{nasim sadat mosavi_2023, title={therapeutic alliance_ clients and therapists}, url={https://www.kaggle.com/dsv/6168984}, DOI={10.34740/KAGGLE/ DSV/6168984}, publisher={Kaggle}, author={Nasim Sadat Mosavi}, year={2023}}. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Data mining techniques in psychotherapy: applications for studying therapeutic allianceCiências Sociais::PsicologiaEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaData has been uploaded to the public repository; Kaggle. @misc{nasim sadat mosavi_2023, title={therapeutic alliance_ clients and therapists}, url={https://www.kaggle.com/dsv/6168984}, DOI={10.34740/KAGGLE/ DSV/6168984}, publisher={Kaggle}, author={Nasim Sadat Mosavi}, year={2023}}.Therapeutic Alliance (TA) has been consistently reported as a robust predictor of therapy outcomes and is one of the most investigated therapy relational factors. Research on therapists' and clients’ contributions to the alliance development and the alliance-outcome relationship had shown mixed results. The relation of the therapist’s and client’s biological markers with the alliance is an important and under-investigated topic. Taking advantage of data mining techniques, this exploratory study aimed to investigate the role of different therapist and client factors, including heart rate (HR) and electrodermal activity (EDA), in relation to TA. Twenty-two dyads with 6 therapists and 22 clients participated in the study. The Working Alliance Inventory (WAI) was used to evaluate the client’s and therapist's perception of the alliance at the end of each session and through the therapy processes. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to explore patterns that may contribute to TA. Machine Learning (ML) models have been employed to provide insights into the predictors and correlates of TA. Our results showed that Linear Regression (LR) was the best technique for predicting the therapist’s TA, with client “Diagnostic” and therapy “Termination” being identified as significant predictors of the therapist’s TA. In addition, for clients’ TA, the Random Forest (RF) was shown to have the best performance. The therapist’s TA and therapy “Outcome” were observed as the most influential predictors for the client’s TA. In addition, while the Heart Rate (therapist) was negatively associated with the therapist’s TA, EDA in the client was a physiological indicator related to the client’s TA. Overall, these findings can assist in identifying key factors that therapists should focus on to enhance the quality of therapeutic alliance. Results are discussed in terms of their consistency with empirical literature, innovative and interdisciplinary research on the therapeutic alliance field, and, in particulaThe work of Eugénia Ribeiro and Adriana Sampaio has been supported by FCT – Fundação para a Ciência e Tecnologia AND Bial Foundation and was conducted at the Psychology Research Centre (CIPsi/UM) School of Psychology, University of Minho, supported by the Foundation for Science and Technology (FCT) through the Portuguese State Budget (UIDB/01662/2020). Furthermore, Nasim Sadat Mosavi and Manuel Filipe Santos have been supported by FCT—Fundação para Ciência e Tecnologia within the R&D Units ( Algoritmi Centre University of Minho, Portugal) Project Scope: UIDB/00319/2020.Nature ResearchUniversidade do MinhoMosavi, NasimsadatRibeiro, EugéniaSampaio, AdrianaSantos, Manuel20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/90409engMosavi, N.S., Ribeiro, E., Sampaio, A. et al. Data mining techniques in psychotherapy: applications for studying therapeutic alliance. Sci Rep 13, 16409 (2023). https://doi.org/10.1038/s41598-023-43366-62045-232210.1038/s41598-023-43366-63777552437775524https://www.nature.com/articles/s41598-023-43366-6info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-11T07:02:20Zoai:repositorium.sdum.uminho.pt:1822/90409Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T07:02:20Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Data mining techniques in psychotherapy: applications for studying therapeutic alliance |
title |
Data mining techniques in psychotherapy: applications for studying therapeutic alliance |
spellingShingle |
Data mining techniques in psychotherapy: applications for studying therapeutic alliance Mosavi, Nasimsadat Ciências Sociais::Psicologia Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
Data mining techniques in psychotherapy: applications for studying therapeutic alliance |
title_full |
Data mining techniques in psychotherapy: applications for studying therapeutic alliance |
title_fullStr |
Data mining techniques in psychotherapy: applications for studying therapeutic alliance |
title_full_unstemmed |
Data mining techniques in psychotherapy: applications for studying therapeutic alliance |
title_sort |
Data mining techniques in psychotherapy: applications for studying therapeutic alliance |
author |
Mosavi, Nasimsadat |
author_facet |
Mosavi, Nasimsadat Ribeiro, Eugénia Sampaio, Adriana Santos, Manuel |
author_role |
author |
author2 |
Ribeiro, Eugénia Sampaio, Adriana Santos, Manuel |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Mosavi, Nasimsadat Ribeiro, Eugénia Sampaio, Adriana Santos, Manuel |
dc.subject.por.fl_str_mv |
Ciências Sociais::Psicologia Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
Ciências Sociais::Psicologia Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
Data has been uploaded to the public repository; Kaggle. @misc{nasim sadat mosavi_2023, title={therapeutic alliance_ clients and therapists}, url={https://www.kaggle.com/dsv/6168984}, DOI={10.34740/KAGGLE/ DSV/6168984}, publisher={Kaggle}, author={Nasim Sadat Mosavi}, year={2023}}. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/90409 |
url |
https://hdl.handle.net/1822/90409 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Mosavi, N.S., Ribeiro, E., Sampaio, A. et al. Data mining techniques in psychotherapy: applications for studying therapeutic alliance. Sci Rep 13, 16409 (2023). https://doi.org/10.1038/s41598-023-43366-6 2045-2322 10.1038/s41598-023-43366-6 37775524 37775524 https://www.nature.com/articles/s41598-023-43366-6 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Nature Research |
publisher.none.fl_str_mv |
Nature Research |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817545181598056448 |