Research Topic Trends on Turnover Intention among Korean Registered Nurses: An Analysis Using Topic Modeling.

Abstract

This study aimed to explore research topic trends on turnover intention among Korean hospital nurses by analyzing the keywords and topics of related articles. Methods: This text-mining study collected, processed, and analyzed text data from 390 nursing articles published between 1 January 2010 and 30 June 2021 that were collected via search engines. The collected unstructured text data were preprocessed, and the NetMiner program was used to perform keyword analysis and topic modeling. Results: The word with the highest degree centrality was "job satisfaction", the word with the highest betweenness centrality was "job satisfaction", and the word with the highest closeness centrality and frequency was "job stress". The top 10 keywords in both the frequency analysis and the 3 centrality analyses included "job stress", "burnout", "organizational commitment", "emotional labor", "job", and "job embeddedness". The 676 preprocessed key words were categorized into five topics: "job", "burnout", "workplace bullying", "job stress", and "emotional labor". Since many individual-level factors have already been thoroughly investigated, future research should concentrate on enabling successful organizational interventions that extend beyond the microsystem.