Identifying Cultural Drivers of Help-Seeking Behavior Using Machine Learning: Stigma Internalization, Norm Salience, and Self-Construal

Authors

    Mariana Téllez Department of Psychology, National Autonomous University of Nicaragua, Managua, Nicaragua
    Olivia Macpherson * Department of Health Psychology, University of Alberta, Edmonton, Canada olivia.macpherson@ualberta.ca
https://doi.org/10.61838/kman.jprfc.5347

Keywords:

Help-seeking behavior, stigma internalization, norm salience, self-construal, machine learning, cultural psychology

Abstract

Objective: 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.

Methods and Materials: 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.

Findings: 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 < .01), and positive associations for norm salience and self-construal (p < .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.

Conclusion: The findings highlight the central role of culturally embedded psychological factors in shaping help-seeking behavior and demonstrate the effectiveness of machine learning approaches.

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Published

2026-01-01

Submitted

2025-07-11

Revised

2025-10-19

Accepted

2025-11-04

How to Cite

Téllez, M., & Macpherson, O. (2026). Identifying Cultural Drivers of Help-Seeking Behavior Using Machine Learning: Stigma Internalization, Norm Salience, and Self-Construal. Journal of Psychosociological Research in Family and Culture, 4(1), 1-10. https://doi.org/10.61838/kman.jprfc.5347