Identifying Predictors of Treatment Dropout Using ML Analysis of Motivational Interviewing Processes and Attachment‑Related Avoidance - Davila Tenorio , Moriel; Coello-Bravo , Jaime; Reyes Zevallos , Micaela (Journal of Assessment and Research in Applied Counseling (JARAC), 2026)
Objective: This study aimed to utilize advanced machine learning algorithms to evaluate whether baseline attachment-related avoidance and specific linguistic frequencies of client change talk and sustain talk during early Motivational Interviewing sessions can reliably predict subsequent outpatient psychotherapeutic treatment dropout. Methods and Materials: The study employed a prospective, longitudinal observational design with a sample of N=485 adults from Mexico City. Dropout was defined as missing three consecutive sessions or mutually agreeing to terminate prematurely. Data collection included baseline demographics, the Experiences in Close Relationships-Revised questionnaire to assess attachment-related avoidance, and Motivational Interviewing Skill Code ratings derived from the audio-recorded and transcribed first two therapy sessions. Machine learning analysis incorporated k-nearest neighbors imputation, Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance, and evaluated Random Forest, Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost) classifiers using stratified 10-fold cross-validation. SHapley Additive exPlanations (SHAP) values were utilized for model interpretability. Findings: Results indicated that 157out of 485participants (32.4%) dropped out of treatment. Bivariate analyses revealed that dropouts had significantly higher attachment-related avoidance (M=4.25) compared to completers (M=3.15). During early sessions, the dropout cohort exhibited significantly more Sustain Talk (M=24.6 vs. M=15.2) and less Change Talk (M=19.8 vs. M=31.4), alongside receiving fewer complex reflections and lower empathy ratings from therapists. The XGBoost model demonstrated superior predictive performance (AUC=0.87), followed by Random Forest (AUC=0.84) and SVM (AUC=0.78). SHAP analyses identified attachment avoidance and patient sustain talk as the strongest overall predictors, highlighting a critical interaction effect where highly avoidant patients receiving fewer therapist complex reflections exhibited a compounding probability of dropout. Conclusion: Machine learning algorithms can accurately predict treatment dropout from early clinical interactions, emphasizing that patient retention fundamentally depends on the complex interplay between severe attachment-related avoidance and the therapist’s technical proficiency in managing resistance.