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Journal of Mental Health Nursing & Statistics

Volume 2, Issue 1, 2026
Mcmed International
Journal of Mental Health Nursing & Statistics
Issn
3117-4345 (Print), 3117-4353 (Online)
Frequency
bi-annual
Email
editorJMHNS@mcmed.us
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Abstract
Title
STATISTICAL MODELING OF DEPRESSION TRENDS: IMPLICATIONS FOR MENTAL HEALTH NURSING INTERVENTIONS
Author
Dr. Manju Lata Adhikari
Email
keyword
Depression, Statistical Modeling, Mental Health Nursing, Predictive Analysis, Risk Factors, Preventive Care, Nursing Interventions, Evidence-Based Practice, Patient-Centered Care
Abstract
Depression is one of the most significant mental health disorders of the 21st century, affecting individuals irrespective of age, gender, culture, or socioeconomic background. It has been recognized as a leading cause of disability worldwide, often resulting in reduced productivity, impaired social functioning, and higher risk of suicide. Mental health nurses are positioned at the frontline of care, and their role in identifying, managing, and preventing depression is indispensable. This study focuses on the use of statistical modeling to understand depression trends, risk factors, and patterns over time, thereby supporting evidencebased nursing interventions. By applying regression analysis, time- series forecasting, and clustering techniques, large-scale datasets can reveal vulnerable populations and key determinants of depression. These insights can then be translated into personalized nursing interventions, such as cognitive behavioral therapy (CBT), stress management workshops, mindfulness practices, peer-support groups, and family counseling. The research integrates quantitative analysis, case study insights, and patient survey questionnaires to explore the dynamic interaction between statistical prediction and nursing practice. Findings highlight the importance of preventive nursing care, patientcentered approaches, and the integration of predictive analytics in mental health strategies. Ultimately, this paperdemonstrates that statistical modeling, when combined with nursing expertise, can revolutionize mental health care delivery and significantly improve recovery outcomes for patients with depression
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