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Invited Paper
- Analysis of Research Topics and Trends in the Journal of Korean Academy of Nursing to Improve Its International Influence
- Yu, Soyoung , Kim, Jeung-Im , Park, Jin-Hee , Jang, Sun Joo , Suh, Eunyoung E. , Song, Ju-Eun , Im, YeoJin
- J Korean Acad Nurs 2020;50(4):501-512. Published online August 31, 2020
- DOI: https://doi.org/10.4040/jkan.20167
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Abstract
PDF - Purpose
The purpose of this study was to analyze articles published in the Journal of the Korean Academy of Nursing (JKAN) between 2010 and 2019, along with those published in three international nursing journals, to improve JKAN’s international reputation.
Methods
The overall characteristics of JKAN’s published papers and keywords, study participants, types of nursing interventions and dependent variables, citations, and cited journals were analyzed. Additionally, the keywords and study designs, publication-related characteristics, journal impact factors (JIF), and Eigenfactor scores of International Journal of Nursing Studies (IJNS), International Nursing Review (INR), Nursing & Health Sciences (NHS), and JKAN were analyzed and compared.
Results
Among the four journals, JKAN’s score was the lowest in both the journal impact factor and Eigenfactor score. In particular, while the JIF of INR and NHS has been continuously increasing; JKAN’s JIF has remained static for almost 10 years. The journals which had cited JKAN and those which JKAN had cited were mainly published in Korean.
Conclusion
JKAN still has a low IF and a low ranking among Social Citation Index (E) journals during the past 10 years, as compared to that of four international journals. To enhance JKAN’s status as an international journal, it is necessary to consider publishing it in English and to continuously improve the conditions of other publications.
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Original Article
- Knowledge Discovery in Nursing Minimum Data Set Using Data Mining
- Myonghwa Park, Jeong Sook Park, Chong Nam Kim, Kyung Min Park, Young Sook Kwon
- Journal of Korean Academy of Nursing 2006;36(4):652-661. Published online March 28, 2017
- DOI: https://doi.org/10.4040/jkan.2006.36.4.652
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Abstract
PDF
Purpose The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making.
Methods Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules.
Results Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality.
Conclusions This study demonstrated the utilization of data mining method through a large data set with stan-dardized language format to identify the contribution of nursing care to patient's health.
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