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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
EVALUATING THE EFFECTIVENESS OF COGNITIVEBEHAVIORAL THERAPY USING PREDICTIVE ANALYTICS IN CLINICAL NURSING PRACTICE
Author
Dr. Madhava Reddy C
Email
keyword
Cognitive-Behavioral Therapy, Predictive Analytics, Clinical Nursing Practice, Mental Health, Depression, Anxiety, Data-Driven Interventions, Nursing Informatics, EvidenceBased Nursing
Abstract
Cognitive-Behavioral Therapy (CBT) has emerged as one of the most effective psychological interventions in clinical nursing practice, particularly in addressing mental health conditions such as depression, anxiety, and stress- related disorders. However, the traditional evaluation of CBT effectiveness relies heavily on subjective measures such as self-reported questionnaires, patient interviews, and observational assessments, which may not always provide accurate or predictive insights into treatment outcomes. With the integration of predictive analytics, nursing professionals now have the ability to examine large sets of patient data, identify behavioral patterns, and predict therapeutic responses with greater accuracy. This paper explores the role of predictive analytics in evaluating CBT effectiveness, with a focus on how nursing practice can be transformed through data-driven decision-making. Using realworld case study models, statistical analysis, and predictive modeling tools, the study highlights how combining nursing expertise with predictive technologies enhances patient care, reduces relapse rates, and supports evidence-based practices. The paper concludes that predictive analytics can serve as a valuable adjunct to traditional CBT evaluation methods, ultimately shaping the future of mental health nursing
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