Posted: Mar 16, 2023
This research investigates the pivotal role of nurses in addressing vaccine hesitancy through a novel computational framework that integrates natural language processing, social network analysis, and behavioral modeling. Unlike traditional public health approaches that focus primarily on educational interventions, our methodology examines the complex interplay between nurse-patient communication patterns, trust dynamics, and information diffusion pathways. We developed a multi-agent simulation environment that models vaccination decision-making processes across diverse hesitant populations, incorporating real-world data from nurse-led vaccination counseling sessions. Our findings reveal that nurses' empathetic communication strategies and personalized risk-benefit framing significantly influence vaccination uptake, with particular effectiveness observed when nurses employ narrative-based approaches rather than statistical evidence alone. The research demonstrates that nurses positioned as trusted intermediaries within social networks can amplify positive vaccination messaging by a factor of 3.7 compared to institutional messaging channels. Furthermore, our analysis identifies specific communication patterns that correlate with successful outcomes, including the timing of interventions relative to patients' information-seeking behaviors and the integration of cultural competency elements. This study contributes a computational foundation for optimizing nurse-led vaccination promotion strategies and provides evidence-based recommendations for healthcare systems seeking to leverage nursing expertise in addressing vaccine hesitancy.
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