Leveraging large language models to identify microcounseling skills in psychotherapy transcripts.
Objective: Microcounseling skills are fundamental to effective psychotherapy, yet manual coding is time- and resource-intensive. This study explores the potential of large language models (LLMs) to automate the identification of these skills in therapy sessions. Method: We fine-tuned GPT-4.1 on a se...
| Publicado en: | Psychotherapy Research Vol. 36; no. 6; pp. 1058 - 1077 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Jul2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=194804733&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 194804733 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10503307 10T jtl: Psychotherapy Research issn: 10503307 maglogo: N pubinfo: dt: Jul2026 vid: 36 iid: 6 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 194804733 10.1080/10503307.2025.2539405 ppf: 1058 ppct: 19 formats: tig: atl: Leveraging large language models to identify microcounseling skills in psychotherapy transcripts. aug: au: Hammerfald, Karin Schmidt, Fabian Vlassov, Vladimir Haaland Jahren, Henrik Solbakken, Ole André affil: Department of Psychology, University of Oslo, Oslo, Norway Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden Braive AS, Oslo, Norway su: Psychotherapy Counseling Generative pre-trained transformers Natural language processing Automatic classification Language models Machine learning sug: subj: Psychotherapy Counseling Other Individual and Family Services Generative pre-trained transformers Natural language processing Automatic classification Language models Machine learning keyword: artificial intelligence counseling skills large language models machine learning natural language processing artificial intelligence counseling skills large language models machine learning natural language processing ab: Objective: Microcounseling skills are fundamental to effective psychotherapy, yet manual coding is time- and resource-intensive. This study explores the potential of large language models (LLMs) to automate the identification of these skills in therapy sessions. Method: We fine-tuned GPT-4.1 on a set of psychotherapy transcripts annotated by human coders. The model was trained to classify therapist utterances, generate explanations for its decisions, and propose alternative responses. The pipeline included transcript preprocessing, dialogue segmentation, and supervised fine-tuning. Results: The model achieved solid performance (Accuracy: 0.78; Precision: 0.79; Recall: 0.78; F1: 0.78; Specificity: 0.77; Cohen's κ: 0.69). It reliably detected common and structurally distinct skills but struggled with more nuanced skills that rely on understanding implicit relational dynamics. Conclusion: Despite limitations, fine-tuned LLMs have potential for enhancing psychotherapy research and clinical practice by providing scalable, automated coding of therapist skills. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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