@inproceedings{katki-etal-2025-automated,
title = "Automated Coding of Counsellor and Client Behaviours in Motivational Interviewing Transcripts: Validation and Application",
author = "Ali, Soliman and
Zhu, Jiading and
Guo, Alex and
Ye, Xiao Nan and
Gu, Qilin and
Wolff, Jodi and
Cooper, Carolynne and
Melamed, Osnat C. and
Selby, Peter and
Rose, Jonathan",
editor = "Zechariah, Arun and
Krishna S, Balu and
Misra Sharma, Dipti and
Mary Thomas, Hannah and
Mammen, Joy and
Krishnamurthy, Parameswari and
Mujadia, Vandan",
booktitle = "NLP-AI4Health",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlpai4health-main.4/",
doi = "10.18653/v1/2025.nlpai4health-main.4",
pages = "25--54",
ISBN = "979-8-89176-315-9",
abstract = "Motivational Interviewing (MI) is a widely-used talk therapy approach employed by clinicians to guide clients toward healthy behaviour change. Both the automation of MI itself and the evaluation of human counsellors can benefit from high-quality automated classification of counsellor and client utterances. We show how to perform this ``coding'' of utterances using LLMs, by first performing utterance-level parsing and then hierarchical classification of counsellor and client language. Our system achieves an overall accuracy of 82{\%} for the upper (coarse-grained) hierarchy of the counsellor codes and 88{\%} for client codes. The lower (fine-grained) hierarchy scores at 68{\%} and 76{\%} respectively. We also show that these codes can be used to predict the session-level quality of a widely-used MI transcript dataset at 87{\%} accuracy. As a demonstration of practical utility, we show that the slope of the amount of change/sustain talk in client speech across 106 MI transcripts from a human study has significant correlation with an independently surveyed week-later treatment outcome ($r=0.28, p<0.005$). Finally, we show how the codes can be used to visualize the trajectory of client motivation over a session alongside counsellor codes. The source code and several datasets of annotated MI transcripts are released."
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<abstract>Motivational Interviewing (MI) is a widely-used talk therapy approach employed by clinicians to guide clients toward healthy behaviour change. Both the automation of MI itself and the evaluation of human counsellors can benefit from high-quality automated classification of counsellor and client utterances. We show how to perform this “coding” of utterances using LLMs, by first performing utterance-level parsing and then hierarchical classification of counsellor and client language. Our system achieves an overall accuracy of 82% for the upper (coarse-grained) hierarchy of the counsellor codes and 88% for client codes. The lower (fine-grained) hierarchy scores at 68% and 76% respectively. We also show that these codes can be used to predict the session-level quality of a widely-used MI transcript dataset at 87% accuracy. As a demonstration of practical utility, we show that the slope of the amount of change/sustain talk in client speech across 106 MI transcripts from a human study has significant correlation with an independently surveyed week-later treatment outcome (r=0.28, p<0.005). Finally, we show how the codes can be used to visualize the trajectory of client motivation over a session alongside counsellor codes. The source code and several datasets of annotated MI transcripts are released.</abstract>
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%0 Conference Proceedings
%T Automated Coding of Counsellor and Client Behaviours in Motivational Interviewing Transcripts: Validation and Application
%A Ali, Soliman
%A Zhu, Jiading
%A Guo, Alex
%A Ye, Xiao Nan
%A Gu, Qilin
%A Wolff, Jodi
%A Cooper, Carolynne
%A Melamed, Osnat C.
%A Selby, Peter
%A Rose, Jonathan
%Y Zechariah, Arun
%Y Krishna S, Balu
%Y Misra Sharma, Dipti
%Y Mary Thomas, Hannah
%Y Mammen, Joy
%Y Krishnamurthy, Parameswari
%Y Mujadia, Vandan
%S NLP-AI4Health
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-315-9
%F katki-etal-2025-automated
%X Motivational Interviewing (MI) is a widely-used talk therapy approach employed by clinicians to guide clients toward healthy behaviour change. Both the automation of MI itself and the evaluation of human counsellors can benefit from high-quality automated classification of counsellor and client utterances. We show how to perform this “coding” of utterances using LLMs, by first performing utterance-level parsing and then hierarchical classification of counsellor and client language. Our system achieves an overall accuracy of 82% for the upper (coarse-grained) hierarchy of the counsellor codes and 88% for client codes. The lower (fine-grained) hierarchy scores at 68% and 76% respectively. We also show that these codes can be used to predict the session-level quality of a widely-used MI transcript dataset at 87% accuracy. As a demonstration of practical utility, we show that the slope of the amount of change/sustain talk in client speech across 106 MI transcripts from a human study has significant correlation with an independently surveyed week-later treatment outcome (r=0.28, p<0.005). Finally, we show how the codes can be used to visualize the trajectory of client motivation over a session alongside counsellor codes. The source code and several datasets of annotated MI transcripts are released.
%R 10.18653/v1/2025.nlpai4health-main.4
%U https://aclanthology.org/2025.nlpai4health-main.4/
%U https://doi.org/10.18653/v1/2025.nlpai4health-main.4
%P 25-54
Markdown (Informal)
[Automated Coding of Counsellor and Client Behaviours in Motivational Interviewing Transcripts: Validation and Application](https://aclanthology.org/2025.nlpai4health-main.4/) (Ali et al., NLP-AI4Health 2025)
ACL
- Soliman Ali, Jiading Zhu, Alex Guo, Xiao Nan Ye, Qilin Gu, Jodi Wolff, Carolynne Cooper, Osnat C. Melamed, Peter Selby, and Jonathan Rose. 2025. Automated Coding of Counsellor and Client Behaviours in Motivational Interviewing Transcripts: Validation and Application. In NLP-AI4Health, pages 25–54, Mumbai, India. Association for Computational Linguistics.