An integrated AHP-TOPSIS multi-criteria decision-making framework found that blended care achieved the highest closeness coefficient (0.979) among three mental health support alternatives evaluated for university students, offering 'the most balanced performance across the selected evaluation criteria.'
Key Findings
Results
Mental Health Need and Expected Effectiveness received the highest priority weight among the five evaluation criteria.
Mental Health Need and Expected Effectiveness was assigned a weight of 0.4847, the highest of all criteria.
User Engagement received the second-highest weight at 0.2268.
Criteria weights were determined using the Analytic Hierarchy Process (AHP) with a five-member expert panel.
The pairwise comparisons demonstrated acceptable consistency with a Consistency Ratio of 0.0221, below the 0.10 threshold typically considered acceptable.
Results
Blended care ranked highest among the three mental health intervention alternatives within the AHP-TOPSIS framework.
Blended care achieved a TOPSIS closeness coefficient of 0.979.
In-person counseling ranked second with a closeness coefficient of 0.402.
Teletherapy ranked third with a closeness coefficient of 0.240.
The ranking reflects comparative performance against defined evaluation criteria and weighting structure, 'rather than direct evidence of clinical effectiveness.'
Results
The ranking of alternatives remained stable under moderate variations in criteria weights, indicating robustness of the framework.
Sensitivity analyses were performed to evaluate the reliability and robustness of the proposed framework.
The ranking order (blended care > in-person counseling > teletherapy) remained stable under moderate variations in the criteria weights.
Consistency analysis confirmed the reliability of the expert panel's pairwise comparisons (CR = 0.0221).
Methods
The study used a publicly available SCL-90 mental health dataset combined with questionnaire responses from 200 university students to operationalize evaluation criteria.
A publicly available SCL-90 mental health dataset was used alongside questionnaire responses.
200 university students provided questionnaire responses.
Five evaluation criteria were identified and operationalized from the empirical evidence and literature.
A five-member expert panel conducted structured evaluations of the three intervention alternatives informed by the empirical evidence.
Conclusions
The integrated AHP-TOPSIS framework was proposed as a transparent and systematic decision-support approach for evaluating university mental health service models.
AHP was used to determine criteria weights through structured pairwise comparisons.
TOPSIS was applied to rank the alternatives based on their similarity to an ideal solution.
The framework combines empirical evidence with structured expert judgment.
The study highlights 'the applicability of integrated multi-criteria decision-making methods to support evidence-informed mental health service planning in higher education.'
What This Means
This research suggests that a structured mathematical decision-making framework can help university administrators and mental health professionals systematically compare different student mental health service options. The study used two established decision-analysis methods (AHP and TOPSIS) to evaluate three types of mental health support — online therapy (teletherapy), traditional face-to-face counseling, and a combination of both (blended care) — against five criteria informed by student survey data and expert judgment. The framework produced clear numerical scores for each option, with blended care scoring far higher (0.979) than in-person counseling (0.402) or teletherapy alone (0.240) within the framework's criteria and weighting structure.
Importantly, the study's authors emphasize that these results reflect how well each service model performs against the selected evaluation criteria — particularly criteria related to mental health need, expected effectiveness, and user engagement — and do not constitute direct clinical evidence that blended care is more therapeutically effective. The expert panel's judgments and the student survey data shaped which criteria were prioritized, meaning the outcome depends heavily on those inputs. The mathematical consistency checks confirmed the expert ratings were reliable, and testing the framework under different assumptions showed the ranking stayed the same.
This research suggests that multi-criteria decision-making tools like AHP-TOPSIS could help higher education institutions make more transparent, evidence-informed choices when designing or selecting mental health services, rather than relying solely on informal judgment. The approach could be adapted to different institutional contexts by adjusting the criteria or seeking input from different stakeholder groups, making it a potentially flexible planning tool for universities facing growing student mental health needs.
Xu Q. (2026). A hybrid AHP-TOPSIS decision-support framework for evaluating mental health support alternatives in higher education.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1861300