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

Computerized natural language processing techniques can analyze psychotherapy sessions as texts, thus generating information about the therapy process and outcome and supporting the scaling-up of psychotherapy research. We used topic modeling to identify topics discussed in psychotherapy sessions and explored (a) which topics best identified clients' functioning and alliance ruptures and (b) whether changes in these topics were associated with changes in outcome. Transcripts of 873 sessions from 58 clients treated by 52 therapists were analyzed. Before each session, clients self-reported functioning and symptom level. After each session, therapists reported the extent of alliance rupture. Latent Dirichlet allocation was used to extract latent topics from psychotherapy textual data. Then a sparse multinomial logistic regression model was used to predict which topics best identified clients' functioning levels and the occurrence of alliance ruptures in psychotherapy sessions. Finally, we used multilevel growth models to explore the associations between changes in topics and changes in outcome. Session-based processing yielded a list of semantic topics. The model identified the labels above chance (65% to 75% accuracy). Change trajectories in topics were associated with change trajectories in outcome. The results suggest that topic models can exploit rich linguistic data within sessions to identify psychotherapy process and outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using computerized text analysis to examine associations between linguistic features and clients' distress during psychotherapy.

Raw linguistic data within psychotherapy sessions may provide important information about clients' progress and well-being. In the current study, computerized text analytic techniques were applied to examine whether linguistic features were associated with clients' experiences of distress within and between clients and whether changes in linguistic features were associated with changes in treatment outcome. Transcripts of 729 psychotherapy sessions from 58 clients treated by 52 therapists were analyzed. Prior to each session, clients reported their distress level. Linguistic features were extracted automatically by using natural language parser for first-person singular identification and using positive and negative emotion words lexicon. The association between linguistic features and levels of distress was examined using multilevel models. At the within-client level, fewer first-person singular words, fewer negative emotional words and more positive emotional words were associated with lower distress in the same session; and fewer negative emotion words were associated with lower next session distress (rather small f2 effect sizes = 0.011 < f2 < 0.022). At the between-client level, only first session use of positive emotion words was associated with first session distress (ηp2 effect size = 0.08). A drop in the use of first-person singular words was associated with improved outcome from pre- to posttreatment (small ηp2 effect size = 0.05). The findings provide preliminary support for the association between clients' linguistic features and their fluctuating experience of distress. They point to the potential value of computerized linguistic measures to track therapeutic outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).