Student Mental Health Screening: A Multidimensional, Digital Approach for Facilitating Early Intervention
Amresh Shrivastava, Manjistha Datta, Avinash DeSousa, Omkar Nayak, Manushree Gupta, Siddhansh Shrivastava, Netra Shukla, Harsh Mange, Janak Limbachia, Sheetal Jagtap, Milind Nemade, Nilesh Shah.
Background: Traditional mental health screening in university settings often relies on single-domain tools focused primarily on diagnosing mental disorders or measuring stress. Such approaches may overlook early subclinical signs of psychological distress, risk factors, and functional impairments that are crucial for timely intervention.
Objective: This study evaluates a multidimensional, digital screening framework designed to detect early mental health concerns among university students in Mumbai.
Method: A cross-sectional digital survey was administered to 442 engineering students using the Mental Health Assessment Scales for Students (MASS) battery. This comprehensive tool includes six validated instruments assessing perceived stress, psychiatric symptoms, environmental risk and vulnerability, resilience, and daily functioning. An algorithm-based digital triage system classified students into categories for self-development, counseling, or psychiatric referral.
Results: The multidimensional screening revealed that 76% of participants reported stress, with 14.3% experiencing severe stress. Notably, 21.1% exhibited clinically significant psychiatric symptoms—such as suicidal ideation, perceptual disturbances, and functional impairment—that were not identified through stress measures alone. Overall, 39.2% showed psychiatric symptoms, 31% demonstrated low resilience, and 36.6% experienced functional impairment. Significant correlations were observed among stress, psychiatric symptoms, resilience, and functioning (p < 0.001), highlighting the interrelated nature of these domains.
Conclusions: Findings indicate that single-domain screening tools substantially underestimate mental health risk among students. A multidimensional, symptom centric digital screening approach provides a more accurate and early identification of at-risk individuals. Integrating such models into university mental health frameworks can enable timely, scalable, and context-sensitive interventions, ultimately improving student well-being.
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