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COVID-19 Pandemic 상황에서 간호대학생의 그릿, 감사성향, 스트레스가 심리적 안녕감에 미치는 영향에 대한 구조모형
최희정(Choi, Heejung),정하림(Jeong, Harim) 한국간호교육학회 2022 한국간호교육학회지 Vol.28 No.1
Purpose: The purposes of this study were to develop and test a model for the effects of grit, gratitude disposition, and stress on the psychological well-being of nursing students during the Coronavirus-2019 pandemic. Methods: The data were collected from June 9 to June 27, 2021. A total of 286 nursing students responded to an online questionnaire. In the hypothesis’s model, the exogenous variables were grit and gratitude disposition, and the endogenous variables were nursing students’ stress and psychological well-being. Data were analyzed using the SPSS/WIN and AMOS programs. Results: The final model showed the following indices of goodness of fit: χ²=78.30, χ²/df=3.01, GFI=.95, CFI=.96, TLI=.94, SRMR=.05, and RMSEA=.08. Nursing students’ psychological well-being was explained by their grit, gratitude disposition, and stress directly and indirectly, with these three variables explaining 56% of psychological well-being. Conclusion: This study identified factors affecting the psychological well-being of nursing students in a state of increased stress due to the COVID-19 pandemic. The results of this study can be a basis for developing and applying a program to enhance nursing students psychological well-being.
한국도로공사 VOC 데이터를 이용한 토픽 모형 적용 방안
김지원(Ji Won Kim),박상민(Sang Min Park),박성호(Sungho Park),정하림(Harim Jeong),윤일수(Ilsoo Yun) 한국IT서비스학회 2020 한국IT서비스학회지 Vol.19 No.6
Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation’s voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as “other”. Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.