Keyword network analysis of changes in the image of nurses pre- and post-COVID-19 in the media environment.

Publication date: Sep 05, 2023

To analyse and describe the changes in the image of nurses perceived by the public pre- and post-COVID-19 in Korea. Nurses play an important role in disaster situations such as COVID-19. In such disaster situations, we aim to confirm the image of nurses projected in the mass media and promote the professionalism of nurses through the image of nurses as professionals. Qualitative media keyword networks analysis. To understand the change in the image of nurses pre- and post-COVID-19, the big data program TEXTOM was used to collect data. From 19 January to 31 May 2020, data were set as ‘post-COVID-19’, and 1 year before 18 January 2020, data were set as ‘pre-COVID-19’. The keywords from 9533 articles were refined, frequency analysed and social networks analysed using TEXTOM and MS office excel, and the analysis results were visualised using UCINET 6 and the NetDraw program. As for keywords related to nurses pre-COVID-19, those with the most frequent appearance and the highest networking degree in centrality were ‘work’, ‘older adults’, ‘care’, ‘time’, ‘care worker’, ‘caring labor’. As for keywords related to nurses post-COVID-19, those that appeared most often and had the highest degree of centrality networking were ‘COVID-19’, ‘USA’, ‘China’, ‘check’, ‘patient’, ‘work’. This study shows that the public image of nurses has changed more positively after COVID-19 due to the media. Individual nurses and nursing organizations should pay attention to the deficiency in the image of nurses and provide a way to reform the public image of professional nurses. The effects of global crises such as COVID-19 on nurses were confirmed, and information delivered through the media was an important way to improve the nursing profession.

Concepts Keywords
China 2019 novel coronavirus disease
Keywords coronavirus disease 2019
Korea COVID-19
Nurses media
network analysis
nurses image


Type Source Name
disease MESH COVID-19
disease VO frequency
disease VO time
disease VO USA

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