NLP-Driven Identification of Communication Deficits Predicting Therapy Dropout in Couples
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".