Min Young Park1, Seok Hee Jeong2, Hee Sun Kim3, Eun Jee Lee3. 1. Department of Nursing, Jeonbuk National University Hospital, Jeonju, Korea. 2. College of Nursing · Research Institute of Nursing Science, Jeonbuk National University, Jeonju, Korea. awesomeprof@jbnu.ac.kr. 3. College of Nursing · Research Institute of Nursing Science, Jeonbuk National University, Jeonju, Korea.
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 "nurse" 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 "a nurse who committed suicide because she could not withstand the Taewoom at work" and "a nurse as a perpetrator of a newborn abuse case," while post-COVID-19 examples are "a nurse as a victim of COVID-19," "a nurse working with the support of the people," and "a nurse as a top contributor and a warrior to protect from COVID-19." 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.
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 "nurse" 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 "a nurse who committed suicide because she could not withstand the Taewoom at work" and "a nurse as a perpetrator of a newborn abuse case," while post-COVID-19 examples are "a nurse as a victim of COVID-19," "a nurse working with the support of the people," and "a nurse as a top contributor and a warrior to protect from COVID-19." 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.
Authors: Judy E Davidson; Rachael Accardi; Courtney Sanchez; Sidney Zisook; Laura A Hoffman Journal: Worldviews Evid Based Nurs Date: 2020-02 Impact factor: 2.931