Research Trends of Articles Published in the Journal of Korean Clinical Nursing Research from 2000 to 2017: Text Network Analysis of Keywords
10.22650/JKCNR.2019.25.1.80
- Author:
Yeon Hee KIM
1
;
Seong Mi MOON
;
In Gak KWON
;
Kwang Sung KIM
;
Geum Hee JEONG
;
Eun Suk SHIN
;
Hyang Soon OH
;
Soo Hyun KIM
Author Information
1. Professor, Department of Clinical Nursing, University of Ulsan, Korea.
- Publication Type:Original Article
- Keywords:
Nursing Research;
Clinical Nursing Research;
Text Network Analysis;
Keyword
- MeSH:
Clinical Nursing Research;
Data Mining;
Nursing;
Nursing Research
- From:
Journal of Korean Clinical Nursing Research
2019;25(1):80-90
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: The aim of this study was to identify the research trends of articles published in the Journal of Korean Clinical Nursing Research from 2000 to 2017 by a text network analysis using keywords. METHODS: This study analyzed 600 articles. The R program was used for text mining that extracted frequency, centrality rank, and keyword network. RESULTS: From 2000 to 2009, keywords with high-frequency were ‘nurse’, ‘pain’, ‘anxiety’, ‘knowledge’, ‘attitude’, and so on. ‘Pain’, ‘nurse’, and ‘knowledge’ showed a high centrality. ‘Fatigue’ showed no high frequency but a high centrality. Keywords such as ‘nurse’, ‘knowledge’, and ‘pain’ also showed high frequency and centrality between 2010 and 2017. ‘Hemodialysis’ and ‘intensive care unit’ were added to keywords with high frequency and centrality during the period. CONCLUSION: The frequency and centrality of keywords such as ‘nurse’, ‘pain’, ‘knowledge’, ‘hemodialysis’, and ‘intensive care unit’ reflect the research trends in clinical nursing between 2000 and 2017. Further studies need to expand the keyword networks by connecting the main keywords.
