Identification of Risk for Mental Health Conditions with a Digital Application: Findings from a Multicentric Study of Students’ Mental Health Screening in Indian Universities

Introduction: Mental health problems among university students represent a major public health concern, with many experiencing subthreshold psychological distress that may progress to clinically diagnosable psychiatric disorders. Traditional symptom screening detects contemporary conditions but does not adequately predict future risk, highlighting the need for more comprehensive and rational screening, for example, using multidimensional assessment, which may detect not only those who are currently suffering from a mental disorder but those as well who are likely to develop such a condition in the future. Materials and Methods: A multicenter study was conducted across three university centers ( n = 600; ages 18–30 years). Participants completed the multidimensional assessment: Mental Health Assessment Scale for Students (MASS) using a Mobile App with a set of psychometric tests, covering six domains: stress, psychiatric symptoms, functional impairment, risk factors, resilience/positivity, and early warning signs. Psychiatric evaluations were conducted within 48 h for validation and clinical triage. Results: Among 600 students, clinical evaluation classified 55% of students as low-to-mild risk (Levels 1–2) and 45% as moderate-to-high risk (Levels 3–4). Digital (MASS) assessment yielded similar results, with 56.2% at Levels 1–2 and 43.8% at Levels 3–4. The composite risk, integrating digital and clinical assessments, identified 53.3% of students as moderate-to-high risk and 46.7% as low-to-mild risk. Psychiatric referrals were flagged in 15.2% of students through digital screening and 13.8% through clinical evaluation; counseling referrals were 26.8% and 38.3%, respectively. Concordance between clinical and composite risk was high (exact agreement 90.6–97.4%; Cohen’s κ 0.72–0.85, P < 0.001), with sensitivity for moderate-to-high risk of 85%–90% and specificity for low-risk students of 81%–92%. Conclusion: MASS provides a reliable, multidimensional, digital approach for early detection and risk stratification of mental health problems in students. The screening method identifies both current mental health conditions as well as future vulnerability, enabling evidence-based triage.