Assessment of Mental Health Risk among University Students in India: A Multidimensional Staging Model

Background: University students face a growing burden of psychological distress, often manifesting as subclinical symptoms that remain undiagnosed and untreated. Traditional clinical approaches fail to address this early stage of mental health deterioration, limiting opportunities for timely intervention. To address this gap, we introduce a staging-based framework for mental health risk stratification using a digital, psychometrically validated tool—Mental Health Assessment Scales for Students (MASS). Objective: This study aims to operationalize a staging model of mental health in university populations by assessing risk indicators, early symptoms, stress levels, functional decline, and resilience. We identify individuals across a continuum from well-being to illness and evaluate MASS as a digital screening mechanism for scalable early intervention. Method: A cross-sectional digital screening was conducted among 442 university students using six MASS scales: Severity of Stress, Psychiatric Symptoms, Mental Health Risk Checklist, Risk and Protective Factors Inventory, Positive Mental Health, and Functioning & Well-Being. Scores were analyzed to classify students into four mental health stages (Stage 1: Healthy/Resilient, Stage 2: At Risk, Stage 3: Symptomatic, Stage 4: Functionally Impaired). Correlation and group-level comparisons were performed to assess the psychometric robustness and clinical relevance of the staging model. Results: 6% of students met criteria for Level 4, exhibiting severe symptoms, significant functional impairment, and acute warning signs including 2.7% reporting suicidal ideation. These students required urgent psychiatric intervention. 22.2% fell under Level 3, marked by moderate to severe psychological distress, subclinical depression and anxiety, poor coping skills, and notable functional decline. 31% were classified as Level 2, with early symptoms and reduced functioning but no diagnosable disorder, indicating a critical need for preventive counseling and psychoeducation. 29.1% were categorized as Level 1, showing healthy functioning, high resilience, and strong protective factors. These students represent a potential peer-support resource. Additionally, 10% of all students exhibited significant psychiatric warning symptoms, such as mood swings, hallucinations, or insomnia. 22% had clinically significant mental health symptoms, validating the presence of hidden psychological morbidity in university environments. Discussion: These findings reveal a stratified pattern of mental health need, emphasizing the importance of staging in early detection and tailored intervention. The presence of severe symptoms and suicidal ideation in a significant minority underscores the need for embedded psychiatric services. Meanwhile, a large at-risk population supports the expansion of counseling, peer programs, and digital tools. Importantly, MASS successfully identifies students functioning below diagnostic thresholds yet vulnerable to psychological decline, demonstrating its value in public mental health planning. Conclusion: The MASS staging model provides an effective framework for identifying varying levels of mental health risk in student populations. By capturing both clinical and preclinical states through a digital, multidimensional approach, it enables targeted, scalable interventions and advances the integration of mental health services within educational institutions.