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Analysis of Behavior Classification in Motivational Interviewing.

Leili Tavabi1, Trang Tran1, Kalin Stefanov2

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Summary
This summary is machine-generated.

This study explored automatic classification of client behaviors in Motivational Interviewing (MI) therapy sessions. RoBERTa embeddings outperformed LIWC features, achieving a 0.66 F1 score for behavior classification.

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Area of Science:

  • Psychology
  • Computer Science
  • Natural Language Processing

Background:

  • Analyzing client and therapist behavior in counseling sessions offers insights into session quality and client outcomes.
  • Standardized behavior codes (annotations) are used to assess psychotherapy sessions, particularly in Motivational Interviewing (MI).

Purpose of the Study:

  • To develop and compare models for automatic classification of client behaviors in MI sessions.
  • To evaluate the performance of models trained on large pretrained embeddings (RoBERTa) versus interpretable expert-selected features (LIWC).

Main Methods:

  • Developed and trained classification models using RoBERTa embeddings and LIWC features.
  • Conducted subject-independent 3-class classification of client behaviors.
  • Performed statistical analysis to identify key LIWC features.

Main Results:

  • The RoBERTa-based model achieved a superior F1 score of 0.66, outperforming the baseline model.
  • Statistical analysis revealed prominent LIWC features potentially missed by the RoBERTa model.
  • Classification using LIWC features alone underperformed compared to RoBERTa.

Conclusions:

  • Pretrained embeddings like RoBERTa show strong performance in classifying client behaviors in MI sessions.
  • Interpretable features from LIWC offer insights but do not match the performance of large pretrained models.
  • Future research should explore incorporating auxiliary tasks to enhance MI code classification.