Journal of Korean Academy of Nursing

Research Paper

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

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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Original Articles

Identification of Knowledge Structure of Pain Management Nursing Research Applying Text Network Analysis: Chan Sook Park, Eun-Jun Park; J Korean Acad Nurs 2019;49(5):538-549. Published online January 15, 2019; DOI: https://doi.org/10.4040/jkan.2019.49.5.538

Abstract PDF

Abstract Purpose

This study aimed to explore and compare the knowledge structure of pain management nursing research, between Korea and other countries, applying a text network analysis.

Methods

321 Korean and 6,685 international study abstracts of pain management, published from 2004 to 2017, were collected. Keywords and meaningful morphemes from the abstracts were analyzed and refined, and their co-occurrence matrix was generated. Two networks of 140 and 424 keywords, respectively, of domestic and international studies were analyzed using NetMiner 4.3 software for degree centrality, closeness centrality, betweenness centrality, and eigenvector community analysis.

Results

In both Korean and international studies, the most important, core-keywords were “pain,” “patient,” “pain management,” “registered nurses,” “care,” “cancer,” “need,” “analgesia,” “assessment,” and “surgery.” While some keywords like “education,” “knowledge,” and “patient-controlled analgesia” found to be important in Korean studies; “treatment,” “hospice palliative care,” and “children” were critical keywords in international studies. Three common sub-topic groups found in Korean and international studies were “pain and accompanying symptoms,” “target groups of pain management,” and “RNs’ performance of pain management.” It is only in recent years (2016~17), that keywords such as “performance,” “attitude,” “depression,” and “sleep” have become more important in Korean studies than, while keywords such as “assessment,” “intervention,” “analgesia,” and “chronic pain” have become important in international studies.

Conclusion

It is suggested that Korean pain-management researchers should expand their concerns to children and adolescents, the elderly, patients with chronic pain, patients in diverse healthcare settings, and patients’ use of opioid analgesia. Moreover, researchers need to approach pain-management with a quality of life perspective rather than a mere focus on individual symptoms.

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A Comparison of Hospice Care Research Topics between Korea and Other Countries Using Text Network Analysis: Eun-Jun Park, Youngji Kim, Chan Sook Park; J Korean Acad Nurs 2017;47(5):600-612. Published online October 31, 2017; DOI: https://doi.org/10.4040/jkan.2017.47.5.600

Abstract PDF

Purpose

This study aimed to identify and compare hospice care research topics between Korean and international nursing studies using text network analysis.

Methods

The study was conducted in four steps: 1) collecting abstracts of relevant journal articles, 2) extracting and cleaning keywords (semantic morphemes) from the abstracts, 3) developing co-occurrence matrices and text-networks of keywords, and 4) analyzing network-related measures including degree centrality, closeness centrality, betweenness centrality, and clustering using the NetMiner program. Abstracts from 347 Korean and 1,926 international studies for the period of 1998–2016 were analyzed.

Results

Between Korean and international studies, six of the most important core keywords-“hospice,” “patient,” “death,” “RNs,” “care,” and “family”-were common, whereas “cancer” from Korean studies and “palliative care” from international studies ranked more highly. Keywords such as “attitude,” “spirituality,” “life,” “effect,” and “meaning” for Korean studies and “communication,” “treatment,” “USA,” and “doctor” for international studies uniquely emerged as core keywords in recent studies (2011~2016). Five subtopic groups each were identified from Korean and international studies. Two common subtopics were “hospice palliative care and volunteers” and “cancer patients.”

Conclusion

For a better quality of hospice care in Korea, it is recommended that nursing researchers focus on study topics of patients with non-cancer disease, children and family, communication, and pain and symptom management.

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Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service: Minji Kim, Mona Choi, Yoosik Youm; J Korean Acad Nurs 2017;47(6):806-816. Published online January 15, 2017; DOI: https://doi.org/10.4040/jkan.2017.47.6.806

Abstract PDF

Abstract Purpose

As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis.

Methods

The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword ‘comprehensive nursing care service’ using Python. A morphological analysis was performed using KoNLPy. Nodes on a ‘comprehensive nursing care service’ cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network.

Results

A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, ‘nursing workforce’ and ‘nursing service’ were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were ‘National Health Insurance Service’ and ‘comprehensive nursing care service hospital.’ The nodes with the highest edge weight were ‘national health insurance,’ ‘wards without caregiver presence,’ and ‘caregiving costs.’ ‘National Health Insurance Service’ was highest in degree centrality.

Conclusion

This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

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