Evaluating the Effectiveness of Cognitive Behavioral Interventions in Reducing Nurse Anxiety Levels
Posted: May 10, 2019
Abstract
This research presents a novel computational framework for evaluating cognitive behavioral therapy (CBT) interventions in reducing anxiety among nursing professionals through a hybrid machine learning and psychometric assessment approach. Traditional studies in healthcare worker mental health have relied primarily on self-report measures and qualitative assessments, lacking the computational rigor and predictive capabilities needed for personalized intervention planning. Our methodology integrates natural language processing of therapeutic session transcripts with physiological monitoring data to create a multi-modal anxiety assessment system. We developed a specialized recurrent neural network architecture that processes both linguistic patterns from therapy sessions and biometric data from wearable sensors to predict anxiety reduction trajectories. The study involved 247 nurses across three hospital systems who participated in a 12-week CBT program while wearing biometric monitoring devices. Our computational model achieved 89.3
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