Machine Learning Prediction of Vocational Success Among Young Adults with Intellectual Disabilities: The Roles of Self-Determination, Career Adaptability, and Family Empowerment
Keywords:
intellectual disabilities, vocational success, self-determination, career adaptability, family empowerment, machine learningAbstract
The present study aimed to develop and evaluate an explainable machine learning model for predicting vocational success among young adults with intellectual disabilities based on self-determination, career adaptability, and family empowerment. This cross-sectional predictive study was conducted among 428 young adults with mild to moderate intellectual disabilities recruited from vocational rehabilitation centers, supported employment programs, and transition-to-work services across Canada. Participants completed standardized measures of vocational success, self-determination, career adaptability, and family empowerment. Data preprocessing procedures included standardization, missing-value imputation, and quality screening. Several machine learning algorithms, including Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost), were trained and evaluated using a 70:30 training-testing split and five-fold cross-validation. Model performance was assessed using the coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHapley Additive exPlanations (SHAP) analysis was employed to identify feature importance and improve interpretability of model predictions. Significant positive associations were observed between vocational success and self-determination (r = .68, p < .001), career adaptability (r = .61, p < .001), and family empowerment (r = .54, p < .001). Among the predictive models tested, XGBoost demonstrated the highest predictive accuracy, accounting for 76% of the variance in vocational success (R² = .76), with the lowest prediction errors (RMSE = 6.94, MAE = 5.13, MAPE = 6.95%). SHAP analysis revealed that self-determination was the most influential predictor, contributing 41.5% of total model importance, followed by career adaptability (30.4%) and family empowerment (22.7%). Collectively, these three variables accounted for more than 94% of the predictive contribution within the final model. The findings indicate that vocational success among young adults with intellectual disabilities can be predicted with high accuracy using machine learning techniques. Self-determination emerged as the strongest predictor, followed by career adaptability and family empowerment, highlighting the importance of both personal agency and supportive family environments. The results support the use of explainable artificial intelligence approaches in disability and vocational research and suggest that interventions targeting these psychosocial factors may enhance employment outcomes and long-term vocational success.
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