ArticleJournal of Korean Academy of Nursing2022
[Images of Nurses Appeared in Media Reports Before and After Outbreak of COVID-19: Text Network Analysis and Topic Modeling].
Article in Journal of Korean Academy of Nursing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- What do patients value? A retrospective study of compliment letters from a single institution.BMJ open · 2026Article
- A Text Network Analysis of Research on the Bereaved After Sudden Death Since 2000.Journal of hospice and palliative care · 2025Article
- Media Portrayals of Nurse Retention: A Decade of News With Topic Modeling and Network Analysis.International nursing review · 2025Article
- Shifting social perceptions of dietitians in Korea after the legislation of nutrition teachers: a keyword network analysis of unstructured data.Korean journal of community nutrition · 2025Article
- Honoring donors: medical students' reflections on cadaveric dissection.BMC medical education · 2025Article
- Medical students' perceptions of improving physician satisfaction and patient care: a text network analysis approach.BMC medical education · 2024Article
- Impact of a game-based interprofessional education program on medical students' perceptions: a text network analysis using essays.BMC medical education · 2024Article
- Agendas on Nursing in South Korea Media: Natural Language Processing and Network Analysis of News From 2005 to 2022.Journal of medical Internet research · 2024Article
- Analysis of issues related to nursing law: Examination of news articles using topic modeling.PloS one · 2024Article
- [National Petition Analysis Related to Nursing: Text Network Analysis and Topic Modeling].Journal of Korean Academy of Nursing · 2023Article
- Research trends over 10 years (2010-2021) in infant and toddler rearing behavior by family caregivers in South Korea: text network and topic modeling.Child health nursing research · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe 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.
methodsData 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.
resultsThe 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" and "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."
conclusionTopic 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.