A hybrid AHP-TOPSIS decision-support framework for evaluating mental health support alternatives in higher education.

Background: Mental health challenges among university students have increased substantially, creating a need for transparent and systematic approaches to support decision-making in the selection of mental health service models. This study proposes an integrated Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) framework to evaluate alternative mental health support systems in higher education. Methods: A publicly available SCL-90 mental health dataset and questionnaire responses from 200 university students were used to identify and operationalize five evaluation criteria. The comparative performance of three intervention alternatives, teletherapy, in-person counseling, and blended care, was assessed through structured evaluation by a five-member expert panel informed by the empirical evidence and the literature. Criteria weights were determined using AHP, and TOPSIS was applied to rank the alternatives. Consistency and sensitivity analyses were performed to evaluate the reliability and robustness of the proposed framework. Results: Mental Health Need and Expected Effectiveness (0.4847) and User Engagement (0.2268) received the highest priority weights. Within the proposed decision support framework, blended care achieved the highest closeness coefficient (0.979), followed by in-person counseling (0.402) and teletherapy (0.240). The pairwise comparisons demonstrated acceptable consistency (Consistency Ratio = 0.0221), and the ranking remained stable under moderate variations in the criteria weights. The findings reflect the comparative performance of the alternatives against the defined evaluation criteria and weighting structure, rather than direct evidence of clinical effectiveness. Conclusion: The proposed AHP and TOPSIS framework provides a transparent and robust decision support approach for evaluating university mental health service models. The results indicate that blended care offers the most balanced performance across the selected evaluation criteria within the proposed framework. The study demonstrates the applicability of integrated multi-criteria decision-making methods to support evidence-informed mental health service planning in higher education, while highlighting the importance of combining empirical evidence with structured expert judgment.