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Using topic models to identify clients' functioning levels and alliance ruptures in psychotherapy.

Dana Atzil-Slonim1, Daniel Juravski2, Eran Bar-Kalifa1

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Natural language processing (NLP) can analyze psychotherapy sessions to reveal key topics. These topics accurately identify client functioning and alliance ruptures, and their changes correlate with therapy outcomes.

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Area of Science:

  • Psychology
  • Computational Linguistics
  • Psychotherapy Research

Background:

  • Computerized natural language processing (NLP) offers a scalable method for analyzing psychotherapy sessions.
  • Analyzing session transcripts can yield insights into therapeutic processes and outcomes.
  • Topic modeling is a technique that can identify latent themes within textual data.

Purpose of the Study:

  • To identify topics within psychotherapy sessions using topic modeling.
  • To determine which identified topics best predict client functioning and alliance ruptures.
  • To investigate the association between changes in topics and changes in therapeutic outcomes.

Main Methods:

  • Utilized latent Dirichlet allocation (LDA) to extract semantic topics from 873 psychotherapy session transcripts.
  • Employed sparse multinomial logistic regression to link topics with client functioning and alliance ruptures.
  • Applied multilevel growth models to assess the relationship between topic changes and outcome changes.

Main Results:

  • Topic modeling successfully identified semantic topics within psychotherapy sessions with above-chance accuracy (65-75%).
  • Specific topics were found to be significant predictors of client functioning and alliance ruptures.
  • Changes in the prevalence of certain topics over time were significantly associated with changes in therapeutic outcomes.

Conclusions:

  • Topic models effectively leverage linguistic data from psychotherapy sessions to identify key process and outcome indicators.
  • NLP techniques, specifically topic modeling, show promise for advancing psychotherapy research and practice.
  • This approach supports the scaling-up of psychotherapy research by providing objective measures from session content.