@article{ref1,
title="Images of Nurses Appeared in Media Reports Before and After Outbreak of COVID-19:Text Network Analysis and Topic Modeling",
journal="Journal of Korean Academy of Nursing",
year="2022",
author="Park, Min-Young and Jeong, Seok-Hee and Kim, Hee-Sun and Lee, Eun-Jee",
volume="",
number="",
pages="291-307",
abstract="PURPOSE@#The aims of study were to identify the main keywords, the network structure, and the main topics of press articles related to nurses that have appeared in media reports. @*Methods@#Data were media articles related to the topic &quot;nurse&quot; reported in 16 central media within a one-year period spanning July 1, 2019 to June 30, 2020. Data were collected from the Big Kinds database. A total of 7,800 articles were searched, and 1,038 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. @*Results@#The number of media reports related to nurses increased by 3.86 times after the novel coronavirus (COVID-19) outbreak compared to prior. Pre- and post-COVID-19 network characteristics were density 0.002, 0.001; average degree 4.63, 4.92; and average distance 4.25, 4.01, respectively. Four topics were derived before and after the COVID-19 outbreak, respectively. Pre-COVID-19 example topics are &quot;a nurse who committed suicide because she could not withstand the Taewoom at work&quot; andf &quot;a nurse as a perpetrator of a newborn abuse case,&quot; while post-COVID-19 examples are &quot;a nurse as a victim of COVID-19,&quot; &quot;a nurse working with the support of the people,&quot; and &quot;a nurse as a top contributor and a warrior to protect from COVID-19.&quot; @*Conclusion@#Topic modeling shows that topics become more positive after the COVID-19 outbreak. Individual nurses and nursing organizations should continuously monitor and conduct further research on nurses' image.<p /><p>Language: en</p>",
language="en",
issn="2005-3673",
doi="10.4040/jkan.22002",
url="http://dx.doi.org/10.4040/jkan.22002"
}