<p>The global nursing shortage is one of the most important issues for the sustainability of healthcare systems, and the pandemic has accelerated the conditions of inconsistency and uncertainty in the nursing employment environment. It is important that healthcare systems develop good retention strategies and provide a more favorable working environment to improve the shortage. First, identifying the core factors affecting the retention of nursing staff and the relationships among these core factors will be helpful for overall improvement of the system. Therefore, one aim of this study is to identify the key factors affecting nursing staff retention by developing an innovative decision-making model to effectively collect and integrate decision-making opinions taking into consideration inconsistency and uncertainty. First, the dominance-based rough set approach (DRSA) model, a data mining technique, was used to identify the core factors and rules affecting nursing staff retention. Second, the Z-number method was used to address incomplete and inconsistent information in decision-making. In addition, a Rough number method was used to integrate expert opinions to arrive at a consensus. Finally, the Decision-Making Trial and Evaluation (DEMATEL) method was used to explore the core causes affecting the nurses’ willingness to stay. The results of this study are applicable to analyze situations under uncertainty, so that the decision-making results will be closer to real-world conditions. The results will not only help healthcare system decision-makers to effectively understand the core factors and causal relationships affecting nursing staff retention, but also assist healthcare organizations to formulate more effective improvement strategies based on the root causes of the problem to ensure the healthy development of nursing staff confronted with resource constraints.</p>