NLP-Driven Identification of Communication Deficits Predicting Therapy Dropout in Couples

Abstract

This study aimed to determine whether natural language patterns extracted from early couple therapy sessions can accurately predict premature therapy dropout. The study used a mixed quantitative–qualitative design involving 148 couples from Canada who participated in community, private, and university-based couple therapy settings. High-quality audio recordings of the first two therapy sessions were transcribed and preprocessed using advanced natural language processing techniques, including tokenization, lemmatization, turn-level segmentation, and affective, syntactic, and semantic feature extraction via transformer-based models. Communication variables such as emotional disengagement, interruption frequency, partner-focused pronoun use, and demand–withdraw sequences were quantified. Machine-learning models—logistic regression, random forest, gradient boosting, Bi-LSTM networks, and transformer architectures—were trained to predict dropout, defined as termination prior to session four without therapist-approved discontinuation. Model performance metrics included accuracy, precision, recall, F1 score, and ROC-AUC. SHAP values were used to interpret model-level decision patterns. Reflexive thematic analysis of therapist notes complemented quantitative findings to contextualize communication deficits. Inferential analyses revealed significant differences between dropout and treatment-completion groups across multiple linguistic variables, including higher negative affect, lower partner-focused pronouns, greater interruption frequency, and elevated demand–withdraw cycles among dropout couples (all p < .001). Transformer-based models achieved the strongest predictive accuracy (92%) and highest ROC-AUC (0.96), outperforming all traditional and neural baselines. SHAP interpretability demonstrated that emotional disengagement markers, interruption frequency, topic abruptions, and conversational asymmetry were the most influential predictors of dropout. Communication reciprocity declined over time in dropout couples, whereas it increased in treatment completers. Early-session communication deficits captured through natural language processing serve as powerful predictors of premature dropout in couple therapy. Incorporating automated linguistic assessment tools into routine clinical practice may enable earlier identification of at-risk couples and support targeted intervention strategies to reduce attrition.