As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis.

The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword ‘comprehensive nursing care service’ using Python. A morphological analysis was performed using KoNLPy. Nodes on a ‘comprehensive nursing care service’ cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network.

A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, ‘nursing workforce’ and ‘nursing service’ were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were ‘National Health Insurance Service’ and ‘comprehensive nursing care service hospital.’ The nodes with the highest edge weight were ‘national health insurance,’ ‘wards without caregiver presence,’ and ‘caregiving costs.’ ‘National Health Insurance Service’ was highest in degree centrality.

This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

To develop and test the validity and reliability of the Korean version of PES-NWI measuring nursing work environments in hospitals.

The Korean version of the PES-NWI was developed through forward-backward translation techniques, and revision based on feedback from focus groups. An internal consistency reliability and construct validity using confirmatory factor analysis were conducted using SPSS WIN (16.0) and AMOS (18.0). Survey data were collected from 733 nurses who worked in three acute care hospitals in Seoul, South Korea.

The Korean version of PES-NWI showed reliable internal consistency with a Cronbach's alpha for the total scale of .93. Factor loadings of the 29 items on the five subscales ranged from .28 to .85. The five subscales model was validated by confirmatory factor analysis (RMR<.05, CFI>.9).

The findings of this study demonstrate that the Korean version of PES-NWI has satisfactory construct validity and reliability to measure nursing work environments of hospitals in Korea.