Analysis of Headline News about Nurses Before and After the COVID-19 Pandemic

S. Baek, Myonghwa Park
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引用次数: 1

Abstract

Purpose: This study analyzed news titles related to nurses in Korea before and after the Coronavirus disease 2019 (COVID 19) pandemic, and aimed to identify the implications of media reports. Methods: Data from January 2019 to December 2020 were collected from BIGKINDS regarding Korean nurses. Text mining and CONCOR analysis were conducted on the top 30 keywords using TEXTOM and Ucinet 6. Results: From the findings of this study, keywords were related to Taewom and Newborn death in 2019. Additionally, because of COVID-19 and the controversy over the encouragement of President Moon Jae-in, Taewom was included in 2020. Using CONCOR analysis, 6 clusters (characteristics and results of major incidents, the issue related target, Newborn abuse, Taewom, drugs, nursing education) were generated in 2019, and 6 clusters (emergency room, hero, controversy, Taewom, COVID-19, hospital infection) were generated in 2020. Conclusion: Before and after the COVID-19 pandemic, most of the news headlines of nurses consisted of negative keywords, while there were few positive news headlines. In order to improve the image of nurses, it is necessary to continuously analyze media trends and establish strategies accordingly. ©2022 Korean Academy of Nursing Administration.
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COVID-19大流行前后护士头条新闻分析
目的:本研究分析了2019冠状病毒病(COVID - 19)大流行前后韩国护士相关的新闻标题,旨在确定媒体报道的含义。方法:从BIGKINDS收集2019年1月至2020年12月韩国护士的数据。使用TEXTOM和Ucinet 6对前30个关键词进行文本挖掘和CONCOR分析。结果:从本研究的结果来看,关键词与2019年太母和新生儿死亡相关。此外,由于新冠疫情和文在寅总统的鼓励争议,太女在2020年被列入了候选名单。采用CONCOR分析,2019年生成6个聚类(重大事件特征及结果、问题相关对象、新生儿虐待、太母、药物、护理教育),2020年生成6个聚类(急诊室、英雄、争议、太母、COVID-19、医院感染)。结论:新冠肺炎疫情前后,护士新闻标题以负面关键词为主,正面新闻标题较少。为了提高护士的形象,有必要不断分析媒体趋势并制定相应的策略。©2022韩国护理管理学院。
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CiteScore
1.50
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0.00%
发文量
17
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