KMAN Publication Inc. (KMANPUB) Journal of Psychosociological Research in Family and Culture 3041-8550 4 Serial Number 13 2026 01 01 Identifying Cultural Drivers of Help-Seeking Behavior Using Machine Learning: Stigma Internalization, Norm Salience, and Self-Construal Identifying Cultural Drivers of Help-Seeking Behavior Using Machine Learning: Stigma Internalization, Norm Salience, and Self-Construal 1 10 10.61838/kman.jprfc.5347 EN Journal Article 2025 07 11 <p><strong>Objective: </strong>The present study aimed to identify and model the cultural determinants of help-seeking behavior using machine learning techniques, focusing on stigma internalization, norm salience, and self-construal.</p> <p><strong>Methods and Materials: </strong>This cross-sectional predictive study was conducted among 512 adults residing in Canada, selected through a stratified sampling approach to ensure demographic diversity. Data were collected using standardized instruments, including the General Help-Seeking Questionnaire (GHSQ), the Internalized Stigma of Mental Illness Scale (ISMI), a validated Social Norms Salience Scale, and the Self-Construal Scale (SCS). Data preprocessing involved normalization, handling missing values via multiple imputation, and encoding categorical variables. Statistical analyses were performed using SPSS-27 for descriptive and correlational assessments. Machine learning models, including Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting, were implemented in Python using Scikit-learn and XGBoost. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics, with k-fold cross-validation ensuring robustness. Feature importance was examined using SHAP (SHapley Additive Explanations) to determine the relative contribution of predictors.</p> <p><strong>Findings: </strong>The results indicated that stigma internalization was the strongest negative predictor of help-seeking behavior, while norm salience and self-construal showed significant positive predictive effects. Gradient Boosting demonstrated the highest performance (AUC = 0.92), outperforming other models. Correlation analysis revealed significant negative associations between stigma internalization and help-seeking (p &lt; .01), and positive associations for norm salience and self-construal (p &lt; .01). SHAP analysis confirmed the hierarchical importance of predictors, with stigma internalization exerting the greatest influence, followed by norm salience and self-construal, and revealed nonlinear interaction effects among variables.</p> <p><strong>Conclusion: </strong>The findings highlight the central role of culturally embedded psychological factors in shaping help-seeking behavior and demonstrate the effectiveness of machine learning approaches.</p> https://www.journals.kmanpub.com/index.php/jprfc/article/download/5347/9721