KMID : 0806120210510040442
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´ëÇÑ°£È£ÇÐȸÁö 2021 Volume.51 No. 4 p.442 ~ p.453
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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
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Yun Eun-Kyoung
Kim Jung-Ok Byun Hye-Min Lee Guk-Geun
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Abstract
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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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KEYWORD
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Nurses, COVID-19, Newspaper Article, Social Network Analysis
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