Journal of Korean Clinical Nursing Research (임상간호연구)

Hospital Nurses Association (병원간호사회)
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Research Trends of Articles Published in the Journal of Korean Clinical Nursing Research from 2000 to 2017: Text Network Analysis of Keywords

텍스트 네크워크 분석을 이용한 임상간호연구 게재논문의 연구동향 분석: 2000년부터 2017년까지

  • Kim, Yeon Hee (Department of Clinical Nursing, University of Ulsan) ;
  • Moon, Seong Mi (Department of Nursing, University of Ulsan) ;
  • Kwon, In Gak (Graduate School of Clinical Nursing Science, Sungkyunkwan University) ;
  • Kim, Kwang Sung (College of Health & Welfare & Nursing, Chungwoon University) ;
  • Jeong, Geum Hee (Division of Nursing, Hallym University, Research Institute of Nursing Science) ;
  • Shin, Eun Suk (Department of Nursing, Chonnam National University Hospital) ;
  • Oh, Hyang Soon (Department of Nursing, Sunchon National University) ;
  • Kim, Soo Hyun (Department of Nursing, Inha University)
  • Received : 2019.01.31
  • Accepted : 2019.02.22
  • Published : 2019.04.30
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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.

Keywords

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Figure 1. Network of top 15 keywords with high frequency (from 2000 to 2009).

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Figure 2. Network of top 15 keywords with high frequency (from 2010 to 2017).

Table 1. Top 15 Keywords with High Centralities (from 2000 to 2009)

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Table 2. Top 15 Keywords with High Centralities (from 2010 to 2017)

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