J Korean Acad Nurs.  2023 Dec;53(6):635-651. 10.4040/jkan.23052.

National Petition Analysis Related to Nursing: Text Network Analysis and Topic Modeling

• Ko H
• Jeong SH
• Lee EJ
• Kim HS

• KMID: 2549932
• DOI: http://doi.org/10.4040/jkan.23052

Abstract

Purpose
This study aimed to identify the main keyword, network structure, and main topics of the national petition related to “nursing” in South Korea.
Methods
Data were gathered from petitions related to the national petition in Korea Blue House related to the topic “nursing” or “nurse” from August 17, 2017, to May 9, 2022. A total of 5,154 petitions were searched, and 995 were selected for the final analysis. Text network analysis and topic modeling were analyzed using the Netminer 4.5.0 program.
Results
Regarding network characteristics, a density of 0.03, an average degree of 144.483, and an average distance of 1.943 were found. Compared to results of degree centrality and betweenness centrality, keywords such as “work environment,” “nursing university,” “license,” and “education” appeared typically in the eigenvector centrality analysis. Topic modeling derived four topics: (1) “Improving the working environment and dealing with nursing professionals,” (2) “requesting investigation and punishment related to medical accidents,” (3) “requiring clear role regulation and legislation of medical and nonmedical professions,” and (4) “demanding improvement of healthcare-related systems and services.”
Conclusion
This is the first study to analyze Korea's national petitions in the field of nursing. This study's results confirmed both the internal needs and external demands for nurses in South Korea. Policies and laws that reflect these results should be developed.

Keyword

Nurses; Nursing; Social Network Analysis; Data Mining