New insights on a recurring theme: A secondary analysis of nurse turnover using the National Sample Survey of Registered Nurses.

Background: Registered nurse (RN) turnover is a recurring phenomenon that accelerated during COVID-19 and heightened concerns about contributing factors.

Purpose: Provide baseline RN turnover data to which pandemic and future RN workforce turnover behaviors can be compared.

Methods: A cross-sectional, secondary analysis of RN turnover using U.S. National Sample Survey of Registered Nurses 2018 data. Responses from 41,428 RNs (weighted N = 3,092,991) across the United States were analyzed. Sociodemographic, professional, employment, and economic data and weighting techniques were used to model prepandemic RN turnover behaviors.

Discussion: About 17% of the sample reported a job turnover, with 6.2% reporting internal and 10.8% reporting external turnover. The factors common across both internal and external turnover experiences included education, employment settings, and years of nursing experience.

Conclusions: Baseline RN turnover data can help employers and policymakers understand new and recurring nursing workforce trends and develop targeted actions to reduce nurse turnover.