Posted: Mar 22, 2023
This research investigates the complex relationship between continuing education programs and the implementation of evidence-based nursing practices through a novel computational framework that combines natural language processing, network analysis, and machine learning. Unlike previous studies that rely primarily on self-reported survey data, our approach analyzes actual nursing documentation patterns across multiple healthcare institutions to quantify behavioral changes following educational interventions. We developed a unique methodology that extracts and categorizes evidence-based practice indicators from electronic health records, creating a multidimensional assessment of practice adoption that considers both frequency and contextual appropriateness. Our longitudinal analysis of 2,347 nurses across 12 healthcare facilities reveals that the effectiveness of continuing education varies significantly based on organizational culture, individual learning styles, and the specific type of evidence being implemented. The findings demonstrate that traditional one-size-fits-all educational approaches yield suboptimal results, while personalized, context-aware educational interventions show 47
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