토픽 모델링을 이용한 국내 포털사이트 방문간호 기사 내용 분석: 코비드-19 이전과 이후 비교를 중심으로

초록

Purpose: This study aimed to explore the social perception of visiting nursing before and after coronavirus disease (COVID-19). Methods: This survey-based study used online big data for comparative analysis by classifying the keywords related to visiting nursing searched on domestic portal sites before and after COVID-19. Results: According to the results of analyzing the Intertopic Distance Map based on Latent Dirichlet Allocation in this study, four topics were extracted, two each before and after COVID-19. The first topic before the COVID-19 period was termed “the expansion of visiting nursing subjects and services visiting nursing,” while the second was termed “visiting nursing,” which is related to customized welfare. The first topic after the COVID-19 period was termed “the suspension and resumption of visiting nursing services,” while the second was “the development of a non-face-to-face home visit healthcare system”. Conclusion: The results of this study can be used as useful reference data to contribute to future medical service delivery system reform policies starting at the end of COVID-19 and the revitalization of community care for visiting nursing.

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