Posted: Feb 17, 2020
The global healthcare sector faces a critical shortage of nursing professionals, with critical care nursing experiencing particularly severe retention challenges. Traditional approaches to understanding nurse retention have relied heavily on cross-sectional surveys and qualitative interviews, which while valuable, capture only snapshots of complex, dynamic career decisions. This research introduces a novel computational framework that models the longitudinal impact of work-life balance initiatives on critical care nurse retention, addressing limitations of conventional methodologies through agent-based simulation and reinforcement learning algorithms. The escalating nursing shortage represents not merely a workforce management issue but a fundamental threat to healthcare system stability, with critical care units experiencing turnover rates exceeding 20%.
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