텍스트 마이닝과 토픽모델링 분석을 활용한 코로나19와 간호사에 대한 언론기사 분석.
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 pneumo...
| Published in: | Journal of Korean Academy of Community Health Nursing / Jiyeog Sahoe Ganho Hakoeji Vol. 32; no. 4; pp. 467 - 477 |
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| Main Authors: | , , |
| Format: | research tables/charts Journal Article |
| Published: |
Korean Academy of Community Health Nursing
Dec2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154446375&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154446375 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 12259594 H9WV jtl: Journal of Korean Academy of Community Health Nursing / Jiyeog Sahoe Ganho Hakoeji issn: 12259594 maglogo: N pubinfo: dt: Dec2021 vid: 32 iid: 4 pid: 92484 pub: Korean Academy of Community Health Nursing artinfo: ui: 154446375 154446375 154446375 10.12799/jkachn.2021.32.4.467 154446375 ppf: 467 ppct: 10 formats: fmt: @attributes: type: P tig: atl: 텍스트 마이닝과 토픽모델링 분석을 활용한 코로나19와 간호사에 대한 언론기사 분석. aug: au: 안지연 이윤정 이복임 affil: 경인여자대학교 간호학과 부교수. sug: subj: COVID-19 Pandemic China Social Perception Data Mining Human China Workforce Nurses COVID-19 Prevention and Control Personnel Shortage ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: Korean refInfo: holdings: @attributes: islocal: N |
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