Journal of Korean Academy of Nursing

Research Papers

National Petition Analysis Related to Nursing: Text Network Analysis and Topic Modeling: Ko, HyunJung , Jeong, Seok Hee , Lee, Eun Jee , Kim, Hee Sun; J Korean Acad Nurs 2023;53(6):635-651. Published online December 31, 2023; DOI: https://doi.org/10.4040/jkan.23052

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.

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Voice of Customer Analysis of Nursing Care in a Tertiary Hospital: Text Network Analysis and Topic Modeling
Hyunjung Ko, Nara Han, Seulki Jeong, Jeong A Jeong, Hye Ryoung Yun, Eun Sil Kim, Young Jun Jang, Eun Ju Choi, Chun Hoe Lim, Min Hee Jung, Jung Hee Kim, Dong Hyu Cho, Seok Hee Jeong
Journal of Korean Academy of Nursing Administration.2024; 30(5): 529. CrossRef
A Study on Internet News for Patient Safety Campaigns: Focusing on Text Network Analysis and Topic Modeling
Sun-Hwa Shin, On-Jeon Baek
Healthcare.2024; 12(19): 1914. CrossRef

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Keyword Network Analysis and Topic Modeling of News Articles Related to Artificial Intelligence and Nursing: Ha, Ju-Young , Park, Hyo-Jin; J Korean Acad Nurs 2023;53(1):55-68. Published online February 28, 2023; DOI: https://doi.org/10.4040/jkan.22117

Abstract PDF

Purpose
The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing.
Methods
After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4.
Results
As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were ’education,’ ‘medical robot,’ and ‘fourth industry.’ Five topics were derived from news articles related to artificial intelligence and nursing: ‘Artificial intelligence nursing research and development in the health and medical field,’ ‘Education using artificial intelligence for children and youth care,’ ‘Nursing robot for older adults care,’ ‘Community care policy and artificial intelligence,’ and ‘Smart care technology in an aging society.’ Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

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Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration
Mehrdad Maghsoudi, Motahareh Kamrani Shahri, Mehrdad Agha Mohammad Ali Kermani, Rahim Khanizad
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The Impact of Artificial Intelligence-Assisted Learning on Nursing Students' Ethical Decision-making and Clinical Reasoning in Pediatric Care
Hyewon Shin, Jennie C. De Gagne, Sang Suk Kim, Minjoo Hong
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Child Health Nursing Research.2023; 29(3): 182. CrossRef

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Images of Nurses Appeared in Media Reports Before and After Outbreak of COVID-19: Text Network Analysis and Topic Modeling: Park, Min Young , Jeong, Seok Hee , Kim, Hee Sun , Lee, Eun Jee; J Korean Acad Nurs 2022;52(3):291-307. Published online June 30, 2022; DOI: https://doi.org/10.4040/jkan.22002

Abstract PDF

Purpose
The aims of study were to identify the main keywords, the network structure, and the main topics of press articles related to nurses that have appeared in media reports.
Methods
Data were media articles related to the topic “nurse” reported in 16 central media within a one-year period spanning July 1, 2019 to June 30, 2020. Data were collected from the Big Kinds database. A total of 7,800 articles were searched, and 1,038 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4.
Results
The number of media reports related to nurses increased by 3.86 times after the novel coronavirus (COVID-19) outbreak compared to prior. Pre- and post-COVID-19 network characteristics were density 0.002, 0.001; average degree 4.63, 4.92; and average distance 4.25, 4.01, respectively. Four topics were derived before and after the COVID-19 outbreak, respectively. Pre-COVID-19 example topics are “a nurse who committed suicide because she could not withstand the Taewoom at work” andf “a nurse as a perpetrator of a newborn abuse case,” while post-COVID-19 examples are “a nurse as a victim of COVID-19,” “a nurse working with the support of the people,” and “a nurse as a top contributor and a warrior to protect from COVID-19.” Conclusion: Topic modeling shows that topics become more positive after the COVID-19 outbreak. Individual nurses and nursing organizations should continuously monitor and conduct further research on nurses’ image.

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Voice of Customer Analysis of Nursing Care in a Tertiary Hospital: Text Network Analysis and Topic Modeling
Hyunjung Ko, Nara Han, Seulki Jeong, Jeong A Jeong, Hye Ryoung Yun, Eun Sil Kim, Young Jun Jang, Eun Ju Choi, Chun Hoe Lim, Min Hee Jung, Jung Hee Kim, Dong Hyu Cho, Seok Hee Jeong
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Analysis of issues related to nursing law: Examination of news articles using topic modeling
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Agendas on Nursing in South Korea Media: Natural Language Processing and Network Analysis of News From 2005 to 2022
Daemin Park, Dasom Kim, Ah-hyun Park
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Research trends over 10 years (2010-2021) in infant and toddler rearing behavior by family caregivers in South Korea: text network and topic modeling
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The Analysis of Research Trends and Public Awareness of Smart Farms using Text Mining
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National Petition Analysis Related to Nursing: Text Network Analysis and Topic Modeling
HyunJung Ko, Seok Hee Jeong, Eun Jee Lee, Hee Sun Kim
Journal of Korean Academy of Nursing.2023; 53(6): 635. CrossRef
An analysis of Research Published in the Journal of Korean Academy of Psychiatric and Mental Health Nursing from 2013 to 2022 using Text Network Analysis and Topic Modeling
Eun Jo Kim, Kuem-Sun Han
Journal of Korean Academy of psychiatric and Mental Health Nursing.2023; 32(2): 188. CrossRef
Chronological Changes in the Portrayal of Korean Nurses in TV Documentaries
Eunjin Kim, Gumhee Baek, Aram Cho, Mijin Byun
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A topic modeling analysis for Korean online newspapers: Focusing on the social perceptions of nurses during the COVID-19 epidemic period
Soo Jung Chang, Sunah Park, Yedong Son
The Journal of Korean Academic Society of Nursing Education.2022; 28(4): 444. CrossRef

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Interorganizational Networks for Smoking Prevention and Cessation: A Blockmodeling Approach: Park, Eun-Jun , Kim, Hyeongsu , Lee, Kun Sei , Cho, Junghee , Kim, Jin Hyeong , Jeong, Ho Jin , Lee, Ji An; J Korean Acad Nurs 2022;52(2):202-213. Published online April 30, 2022; DOI: https://doi.org/10.4040/jkan.21192

Abstract PDF: Purpose
This study examined characteristics and patterns of interorganizational networks for smoking prevention and cessation in Korea.
Methods
We surveyed two community health centers, ninety-five hospitals or clinics, ninety- two pharmacies, and sixty-five health welfare organizations in two districts of Seoul in 2020. Data on the organizations’ characteristics of smoking cessation and interorganizational activities for information sharing, client referral, and program collaboration were collected and analyzed using network statistics and blockmodeling.
Results
Network size was in the order of information sharing, client referral, and program collaboration networks. Network patterns for interorganizational activities on information sharing, client referral, and program collaboration among four organizations were similar between the two districts. Community health centers provided information and received clients from a majority of the organizations. Their interactions were not unidirectional but mutual with other organizations. Pharmacies were involved in information sharing with health welfare organizations and client referrals to hospitals or clinics. Health welfare organizations were primarily connected with the community health centers for client referrals and program collaboration.
Conclusion
A community health center is the lead agency in interorganizational activities for smoking prevention and cessation. However, hospitals or clinics, pharmacies, and health welfare organizations also participate in interorganizational networks for smoking prevention and cessation with diverse roles. This study would be evidence for developing future interorganizational networks for smoking prevention and cessation.

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Topic Modeling and Keyword Network Analysis of News Articles Related to Nurses before and after “the Thanks to You Challenge” during the COVID-19 Pandemic: Yun, Eun Kyoung , Kim, Jung Ok , Byun, Hye Min , Lee, Guk Geun; J Korean Acad Nurs 2021;51(4):442-453. Published online August 31, 2021; DOI: https://doi.org/10.4040/jkan.20287

Abstract PDF

Purpose
This study was conducted to assess public awareness and policy challenges faced by practicing nurses.
Methods
After collecting nurse-related news articles published before and after ‘the Thanks to You Challenge’ campaign (between December 31, 2019, and July 15, 2020), keywords were extracted via preprocessing. A three-step method keyword analysis, latent Dirichlet allocation topic modeling, and keyword network analysis was used to examine the text and the structure of the selected news articles.
Results
Top 30 keywords with similar occurrences were collected before and after the campaign. The five dominant topics before the campaign were: pandemic, infection of medical staff, local transmission, medical resources, and return of overseas Koreans. After the campaign, the topics ‘infection of medical staff’ and ‘return of overseas Koreans’ disappeared, but ‘the Thanks to You Challenge’ emerged as a dominant topic. A keyword network analysis revealed that the word of nurse was linked with keywords like thanks and campaign, through the word of sacrifice. These words formed interrelated domains of ‘the Thanks to You Challenge’ topic.
Conclusion
The findings of this study can provide useful information for understanding various issues and social perspectives on COVID-19 nursing. The major themes of news reports lagged behind the real problems faced by nurses in COVID-19 crisis. While the press tends to focus on heroism and whole society, issues and policies mutually beneficial to public and nursing need to be further explored and enhanced by nurses.

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Patent Technology Trends of Oral Health: Application of Text Mining
Hee-Kyeong Bak, Yong-Hwan Kim, Han-Na Kim
Journal of Dental Hygiene Science.2024; 24(1): 9. CrossRef
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Daemin Park, Dasom Kim, Ah-hyun Park
Journal of Medical Internet Research.2024; 26: e50518. CrossRef
Analysis of issues related to nursing law: Examination of news articles using topic modeling
JooHyun Lee, Hyoung Eun Chang, Jaehyuk Cho, Seohyun Yoo, Joonseo Hyeon, Andrea Cioffi
PLOS ONE.2024; 19(8): e0308065. CrossRef
Research Trends on Cancer-Related Cognitive Impairment in Patients with Non-Central Nervous System Cancer: Text Network Analysis and Topic Modeling
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Journal of Korean Academy of Fundamentals of Nursing.2023; 30(3): 313. CrossRef
Perspectives of Frontline Nurses Working in South Korea during the COVID-19 Pandemic: A Combined Method of Text Network Analysis and Summative Content Analysis
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Journal of Korean Academy of Nursing.2023; 53(6): 584. CrossRef
Nurses’ Experience in COVID-19 Patient Care
Soojin Chung, Mihyeon Seong, Ju-young Park
Journal of Korean Academy of Nursing Administration.2022; 28(2): 142. CrossRef
A topic modeling analysis for Korean online newspapers: Focusing on the social perceptions of nurses during the COVID-19 epidemic period
Soo Jung Chang, Sunah Park, Yedong Son
The Journal of Korean Academic Society of Nursing Education.2022; 28(4): 444. CrossRef
Images of Nurses Appeared in Media Reports Before and After Outbreak of COVID-19: Text Network Analysis and Topic Modeling
Min Young Park, Seok Hee Jeong, Hee Sun Kim, Eun Jee Lee
Journal of Korean Academy of Nursing.2022; 52(3): 291. CrossRef
Experience of Nurses in Charge of COVID-19 Screening at General Hospitals in Korea
Boo Young Ha, Yun-Sook Bae, Han Sol Ryu, Mi-Kyeong Jeon
Journal of Korean Academy of Nursing.2022; 52(1): 66. CrossRef
An Exploratory Study on Current Nursing Issues in the COVID-19 era through Newspaper Articles: The Application of Text Network Analysis
Young Joo Lee
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Analysis of Headline News about Nurses Before and After the COVID-19 Pandemic
Su-Mi Baek, Myonghwa Park
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Warmth and competence perceptions of key protagonists are associated with containment measures during the COVID-19 pandemic: Evidence from 35 countries
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Scientific Reports.2022;[Epub] CrossRef

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Original Articles

Work Analysis for the Role of the Emergency Department Nurses: Eun Jung Kim; Journal of Korean Academy of Nursing 1998;28(1):93-103. Published online March 29, 2017; DOI: https://doi.org/10.4040/jkan.1998.28.1.93

Abstract PDF

Nursing works in emergency department were analyzed and the importance of nursing works that the emergency department nurses perceived at university hospitals in Seoul. 12 nursing domains including 76 nursing activities were identified. The most frequently performed nursing domain was records and the most frequently performed activity in the emergency department was checking the vital sign of patients. The most important nursing activity that emergency department nurses perceived was physical crisis intervention.

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Kyung Hyeok Song, Sung Uk Cho, Jin Woong Lee, Yong Chul Cho, Won Joon Jeong, Yeon Ho You, Seung Ryu, Seung Whan Kim, In Sool Yoo, Ki Hyuk Joo
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A Study on the Knowledge Structure of Cancer Survivors based on Social Network Analysis: Sun Young Kwon, Ka Ryeong Bae; J Korean Acad Nurs 2016;46(1):50-58. Published online February 29, 2016; DOI: https://doi.org/10.4040/jkan.2016.46.1.50

Abstract PDF

Purpose

The purpose of this study was to identify the knowledge structure of cancer survivors.

Methods

For data, 1099 articles were collected, with 365 keywords as a Noun phrase extracted from the articles and standardized for analyzing. Co-occurrence matrix were generated via a cosine similarity measure, and then the network analysis and visualization using PFNet and NodeXL were applied to visualize intellectual interchanges among keywords.

Results

According to the result of the content analysis and the cluster analysis of author keywords from cancer survivors articles, keywords such as 'quality of life', 'breast neoplasms', 'cancer survivors', 'neoplasms', 'exercise' had a high degree centrality. The 9 most important research topics concerning cancer survivors were 'cancer-related symptoms and nursing', 'cancer treatment-related issues', 'late effects', 'psychosocial issues', 'healthy living managements', 'social supports', 'palliative cares', 'research methodology', and 'research participants'.

Conclusion

Through this study, the knowledge structure of cancer survivors was identified. The 9 topics identified in this study can provide useful research direction for the development of nursing in cancer survivor research areas. The Network analysis used in this study will be useful for identifying the knowledge structure and identifying general views and current cancer survivor research trends.

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A Social Network Analysis of Research Topics in Korean Nursing Science: Soo-Kyoung Lee, Senator Jeong, Hong-Gee Kim, Young-Hee Yom; J Korean Acad Nurs 2011;41(5):623-632. Published online October 31, 2011; DOI: https://doi.org/10.4040/jkan.2011.41.5.623

Abstract PDF

Purpose

This study was done to explore the knowledge structure of Korean Nursing Science.

Methods

The main variables were key words from the research papers that were presented in the Journal of Korean Academy of Nursing and journals of the seven branches of the Korean Academy of Nursing. English titles and abstracts of the papers (n=5,936) published from 1995 through 2009 were included. Noun phrases were extracted from the corpora using an in-house program (BiKE Text Analyzer), and their co-occurrence networks were generated via a cosine similarity measure, and then the networks were analyzed and visualized using Pajek, a Social Network Analysis program.

Results

With the hub and authority measures, the most important research topics in Korean Nursing Science were identified. Newly emerging topics by three-year period units were observed as research trends.

Conclusion

This study provides a systematic overview on the knowledge structure of Korean Nursing Science. The Social Network Analysis for this study will be useful for identifying the knowledge structure in Nursing Science.

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