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안지연 ( An Ji-Yeon ) - Kyungin Women’s University Department of Nursing
이윤정 ( Yi Yun-Jeong ) - Kyungin Women’s University Department of Nursing
이복임 ( Lee Bok-Im ) - University of Ulsan Department of Nursing
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Abstract
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Purpose: The purpose of this study is to understand the social perceptions of nurses in the context of the COVID-19 outbreak through analysis of media articles.
Methods: Among the media articles reported from January 1st to September 30th, 2020, those containing the keywords ‘[corona or Wuhan pneumonia or covid] and [nurse or nursing]’ are extracted. After the selection process, the text mining and topic modeling are performed on 454 media articles using textom version 4.5.
Results: Frequency Top 30 keywords include ‘Nurse’, ‘Corona’, ‘Isolation’, ‘Support’, ‘Shortage’, ‘Protective Clothing’, and so on. Keywords that ranked high in Term Frequency-Inverse Document Frequency (TF-IDF) values are ‘Daegu’, ‘President’, ‘Gwangju’, ‘manpower’, and so on. As a result of the topic analysis, 10 topics are derived, such as ‘Local infection’, ‘Dispatch of personnel’, ‘Message for thanks’, and ‘Delivery of one’s heart’.
Conclusion: Nurses are both the contributors and victims of COVID-19 prevention. The government and the nurses’ community should make efforts to improve poor working conditions and manpower shortages.
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KeyWords
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코로나19, 간호사, 데이터 마이닝
COVID-19, Nurses, Data mining
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원문 및 링크아웃 정보
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등재저널 정보
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