[Images of Nurses Appeared in Media Reports Before and After Outbreak of COVID-19: Text Network Analysis and Topic Modeling].

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.

Academic Success of Online Learning in Undergraduate Nursing Education Programs in the COVID-19 Pandemic Era.

BACKGROUND: This study aimed to investigate predictors for academic success, including satisfaction with online class and academic achievement, in the coronavirus disease 19 (COVID-19) pandemic era. PURPOSE: To obtain basic data needed to improve the quality and outcomes of online learning in lectures for nursing students. METHOD: A cross-sectional, descriptive, nationwide online survey in South Korea was performed using structured questionnaires. Participants were 200 nursing students taking online-based learning at universities in 2020. Data were analyzed using descriptive statistics and hierarchical multiple regression with SPSS WIN 26.0 program. RESULTS: Cyber-class flow (ß = 0.65, p < 0.001) was a significant predictor of satisfaction with online class. Self-directed learning (ß = 0.18, p = 0.014) and satisfaction with online class (ß = 0.19, p = 0.035) were significant predictors of academic achievement. CONCLUSION: To achieve academic success from online learning, self-directed learning should be prioritized and satisfaction with online class needs to be managed by nursing educators. To improve satisfaction with online class, cyber-class flow should be considered when designing teaching and learning methods for undergraduate nursing education programs.