Identification of Cultural Conversations in Therapy Using Natural Language Processing Models

Researchers have historically focused on understanding therapist multicultural competency and orientation through client self-report measures and behavioral coding. While client perceptions of therapist cultural competency and multicultural orientation and behavioral coding are important, reliance on these methods limits therapists receiving systematic, scalable feedback on cultural opportunities within sessions. Prior research demonstrating the feasibility of automatically identifying topics of conversation in psychotherapy suggests that natural language processing (NLP) models could be trained to automatically identify when clients and therapists are talking about cultural concerns and could inform training and provision of rapid feedback to therapists. Utilizing 103,170 labeled talk turns from 188 psychotherapy sessions, we developed NLP models that recognized the discussion of cultural topics in psychotherapy (F − 1 = 70.0; Spearman's ρ = 0.78, p < .001). We discuss implications for research and practice and applications for future NLP-based feedback tools. Clinical Impact Statement Question: Our study focused on developing natural language processing (NLP) models that could identify conversations surrounding race, ethnicity, gender, sexuality, and religion using 103,170 labeled talk turns from 188 psychotherapy sessions. Findings: We developed NLP models that recognized the discussion of cultural topics in psychotherapy (F − 1 = 0.70; Spearman's ρ = 0.78, p < .001). Meaning: Conversations surrounding cultural identities in sessions can be identified at large scales, particularly when models account for more contextual information. Next Steps: Future research should focus on developing NLP models (a) identifying conversations surrounding cultural identities in large, diverse samples, and (b) evaluating patterns in how therapists navigate conversations about cultural identities.