Browse > Article
http://dx.doi.org/10.14370/jewnr.2020.26.2.118

Simulation Nursing Education Research Topics Trends Using Text Network Analysis  

Park, Chan Sook (Department of Nursing Science, College of Health Sciences, Sangji University)
Publication Information
Journal of East-West Nursing Research / v.26, no.2, 2020 , pp. 118-129 More about this Journal
Abstract
Purpose: The purpose of this study was to analyze the topic trend of domestic simulation nursing education research using text network analysis(TNA). Methods: This study was conducted in four steps. TNA was performed using the NetMiner (version 4.4.1) program. Firstly, 245 articles from 4 databases (RISS, KCI, KISS, DBpia) published from 2008 to 2018, were collected. Secondly, keyword-forms were unified and representative words were selected. Thirdly, co-occurrence matrices of keywords with a frequency of 2 or higher were generated. Finally, social network-related measures-indices of degree centrality and betweenness centrality-were obtained. The topic trend over time was visualized as a sociogram and presented. Results: 178 author keywords were extracted. Keywords with high degree centrality were "Nursing student", "Clinical competency", "Knowledge", "Critical thinking", "Communication", and "Problem-solving ability." Keywords with high betweenness centrality were "CPR", "Knowledge", "Attitude", "Self-efficacy", "Performance ability", and "Nurse." Over time, the topic trends on simulation nursing education have diversified. For example, topics such as "Neonatal nursing", "Obstetric nursing", "Pediatric nursing", "Blood transfusion", "Community visit nursing", and "Core basic nursing skill" appeared. The core-topics that emerged only recently (2017-2018) were "High-fidelity", "Heart arrest", "Clinical judgment", "Reflection", "Core basic nursing skill." Conclusion: Although simulation nursing education research has been increasing, it is necessary to continue studies on integrated simulation learning designs based on various nursing settings. Additionally, in simulation nursing education, research is required not only on learner-centered educational outcomes, but also factors that influence educational outcomes from the perspective of the instructors.
Keywords
Patient Simulation; Nursing; Nursing student; Education; Network Meta-Analysis;
Citations & Related Records
Times Cited By KSCI : 25  (Citation Analysis)
연도 인용수 순위
1 Lee J, Jeon J, Kim S. Learning experience of undergraduate nursing students in simulation: A meta-synthesis and meta-ethnography study. Journal of Korean Academic Society of Nursing Education. 2019; 25(3):300-11. https://doi.org/10.5977/jkasne.2019.25.3.300   DOI
2 Park EJ, Ahn DW, Park CS. Text network analysis of newspaper articles on life-sustaining treatments. Journal of Korean Academy of Community Health Nursing. 2018;29(2):244-56. https://doi.org/10.12799/jkachn.2018.29.2.244   DOI
3 Park SH. Text network analysis of the Korean national kindergarten curriculum. Journal of Learner-Centered Curriculum and Instruction. 2018;18:1041-61. https://doi.org/10.22251/jlcci.2018.18.22.1041   DOI
4 Kim MJ, Choi MN, Youm YS. Semantic network analysis of online news and social media text related to comprehensive nursing care service. Journal of Korean Academy of Nursing. 2017;47(6):806-16. https://doi.org/10.4040/jkan.2017.47.6.806   DOI
5 Lee SS. A Content analysis of journal articles using the language network analysis methods. Journal of the Korean Society for Information Management. 2014;31(4):49-68. https://doi.org/10.3743/KOSIM.2014.31.4.049   DOI
6 Cyram. NetMiner version 4.4.1. 4.3 ed. Seoul: Cyram Inc.; 2019.
7 Cyram. Social network analysis using NetMiner: Analysis of literature information. Seoul: Cyram Inc.; 2016. pp.8-72.
8 Lee SS. Network analysis methods. Seoul: Nonhyeong; 2012. pp. 207-268.
9 Wasserman S, Faust K. Social network analysis: Methods and applications. New York, USA: Cambridge University Press; 1994. pp.169-190.
10 Bambini D, Washburn J, Perkins R. Outcomes of clinical simulation for novice nursing students: Communication, confidence, clinical judgment. Nursing Education Perspectives. 2009;30(2):79-82.
11 Hanshaw SL, Dickerson SS. High fidelity simulation evaluation studies in nursing education: A review of the literature. Nurse Education in Practice. 2020;46:102818. https://doi.org/10.1016/j.nepr.2020.102818   DOI
12 Lim KC. Directions of simulation-based learning in nursing practice education: A systematic review. The Journal of Korean Academic Society of Nursing Education. 2011;17(2):246-56. https://doi.org/10.5977/JKASNE.2011.17.2.246   DOI
13 Park SJ, Ji ES. A structural model on the nursing competencies of nursing simulation learners. Journal of Korean Academy of Nursing. 2018;48(5):588-600. https://doi.org/10.4040/jkan.2018.48.5.588   DOI
14 Chu MS, Hwang YY. Effects of web-based simulation and high-fidelity simulation of acute heart disease patient care. The Journal of Korean Academic Society of Nursing Education. 2017;23(1):95-107. https://doi.org/10.5977/jkasne.2017.23.1.95   DOI
15 Jang HJ, Park JS. Effectiveness of simulation problem-based learning for community visit nursing according to Myers Briggs Type Indicator (MBTI) personality types. The Journal of Korean Academic Society of Nursing Education. 2016;22(4):577-87. https://doi.org/10.5977/jkasne.2016.22.4.577   DOI
16 Kim HJ, Chun IH. The effect of problem-based learning and simulation practice convergence education for nursing students. Journal of the Korea Convergence Society. 2018;9(7):355-64. https://doi.org/10.15207/JKCS.2018.9.7.355   DOI
17 Kwon SJ, Kim HD. Effect of a simulation-based education for delivery nursing program on Knowledge, problem solving process and confidence in nursing students. Journal of Korean Society for Simulation in Nursing. 2016;4(1):13-22.
18 Baek HC, Lee YR, Lee JE, Lee JH, Kim HS. Evaluation and application effect of a home nasogastric tube feeding simulation module for nursing students: An application of the NLN Jeffries simulation theory. Journal of Korean Academy of Community Health Nursing. 2017;28(3):324-333. https://doi.org/10.12799/jkachn.2017.28.3.324   DOI
19 Lim KC. Planning and applying simulation-based practice for the achievement of program outcomes in nursing students. The Journal of Korean Academic Society of Nursing Education. 2015;21(3):393-405. https://doi.org/10.5977/jkasne.2015.21.3.393   DOI
20 Jeffries PR. A framework for designing, implementing, and evaluating simulations used as teaching strategies in nursing. Nursing Education Perspectives. 2005;26(2):96-103.
21 Lee SK, Jeong S, Kim HG, Yom YH. A social network analysis of research topics in Korean nursing science. Journal of Korean Academy of Nursing. 2011;41(5):623-32. https://doi.org/10.4040/jkan.2011.41.5.623   DOI
22 Suh EY. Development of a conceptual framework for nursing simulation education utilizing human patient simulators and standardized patients. The Journal of Korean Academic Society of Nursing Education. 2012;18(2):206-19. https://doi.org/10.5977/jkasne.2012.18.2.206   DOI
23 Korean Accreditaion Board of Nursing Education(KABONE). Nursing education certification criteria [Internet]. Seoul: KABONE; 2017 [cited 2020 Jan 16]. Available from: http://www.kabone.or.kr/
24 Kang SJ, Hong CM, Lee H. The impact of virtual simulation on critical thinking and self-directed learning ability of nursing students. Clinical Simulation in Nursing. 2020;20:1-7. https://doi.org/10.1016/j.ecns.2020.05.008   DOI
25 Kim JH, Park IH, Shin SJ. Systematic review of Korean studies on simulation within nursing education. The Journal of Korean Academic Society of Nursing Education. 2013;19(3):307-19. https://doi.org/10.5977/jkasne.2013.19.3.307   DOI
26 Kim SH, Ham YS. A meta-analysis of the effect of simulation based education - Korean nurses and nursing students. The Journal of Korean Academic Society of Nursing Education. 2015;21(3):308-19. https://doi.org/10.5977/jkasne.2015.21.3.308   DOI
27 Kim YJ, Jang SN. Mapping the knowledge structure of frailty in journal articles by text network analysis. PloS One. 2018;13(4):e0196104. https://doi.org/10.1371/journal.pone.0196104   DOI
28 Park CS, Jung JW. Text network analysis: Detecting shared meaning through socio-cognitive networks of policy stakeholders. Journal of Governmental Studies. 2013;19(2):73-108.
29 Park EJ, Kim YJ, Park CS. A comparison of hospice care research topics between Korea and other countries using text network analysis. Journal of Korean Academy of Nursing. 2017;47(5):600-12. https://doi.org/10.4040/jkan.2017.47.5.600   DOI