• Journal of Korean Society of Rural Planning
• /
• v.25 no.2
• /
• pp.89-98
• /
• 2019

The purpose of this study is to develop the evaluation factors for formation of the future - oriented rural residential environment and to derive the importance of this evaluation factors. The main research methods are network analysis using Net-miner, AHP analysis and importance analysis for experts. The main research results are as follows. First, 6 factors for evaluation rural residential environment(environmental-sustainability, communality, self-reliance, aesthetic, enjoyment, settlement), 3 sub-factors of each factors, and key physical facilities(10 for rural center and 28 for rural villages) were derived by literature analysis and network analysis on it. Second, key priority factors that should be considered in physical formation of rural residential environment was deducted by AHP analysis. Improving accessibility of living services in the settlement factor, improvement of garbage collection and wastewater treatment system around the residential area, and ensuring topographic continuity in the environmental-sustainability factor was derived as a priority factors. Third, as a result of evaluation of the importance of physical facilities in the rural residential environment, there was a high demand for cultural welfare facilities in common. However, when compared with the villages where the harmony between the natural environment and the living environment is emphasized, convenience of living and accessibility of transportation were important in the rural center. These results suggest that solving the qualitative conditions as residential environment will help to attract new population. This study will contribute to enhance the qualitative level of the rural residential environment by suggesting strategic priority items when carrying out projects related to the rural residential environment creation in the future.

• Journal of the Korea Convergence Society
• /
• v.10 no.4
• /
• pp.229-236
• /
• 2019

The purpose of this paper is to analyze social phenomena and perceptions by collecting and analyzing data on public opinion, views and trends related to special case of military service in the sports community through Big KINDS operated by the Korea Press Promotion Foundation. To this end, the related keywords were derived and visualized by implementing a LDA(latent dirichlet allocation) technique to derive problems found in social phenomena based on big data analysis. The topics derived include "re-lighting special case on military service," " military service corruption controversy," "special case of military service for athletes," "alternative military service system for artists " and "parliamentary inspection of the administration" This could be used as a basic data for identifying accurate information on social controversies related to special case of military service in the sports community and drawing up practical measures that are considered in line with the principle of just and equal burden.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3571-3587
• /
• 2021

For accurately analysing and forecasting the social networks of China's political, economic and social power elites, it is necessary to develop a database that collates their information. The development of such a database involves three stages: data definition, data collection and data quality maintenance. The present study recommends distinctive solutions in overcoming the challenges that occur in existing comparable databases. We used organizational and event factors to identify the Chinese power elites to be included in the database, and used their memberships, social relations and interactions in combination with flows data collection methodologies to determine the associations between them. The system can be used to determine the optimal relationship path (i.e., the shortest path) to reach a target elite and to identify of the most important power elite in a social network (e.g., degree, closeness and eigenvector centrality) or a community (e.g., a clique or a cluster). We have used three social network analysis tools (i.e., R, UCINET and NetMiner) in order to find the important nodes in the network. We compared the results of centrality rankings of each tool. We found that all three tools are providing slightly different results of centrality. This is because different tools use different algorithms and even within the same tool there are various libraries which provide the same functionality (i.e., ggraph, igraph and sna in R that provide the different function to calculate centrality). As there are chances that the results may not be the same (i.e. centrality rankings indicating the most important nodes can be varied), we recommend a comparison test using different tools to get accurate results.

• Journal of Korean Academy of Nursing
• /
• v.52 no.3
• /
• pp.291-307
• /
• 2022

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.

• Human Ecology Research
• /
• v.60 no.1
• /
• pp.1-20
• /
• 2022

This paper presents a social network analysis of changes in Home Economics education content loaded on YouTube before and after the outbreak of COVID-19. From January 1, 2008 to June 30, 2021, a basic analysis was conducted of 761 Home Economics education videos loaded on YouTube, using NetMiner 4.3 to analyze important keywords and the centrality of video titles and full texts. Before COVID-19, there were 164 Home Economics education videos posted on YouTube, increasing significantly to 597 following the emergence of the pandemic. In both periods, there was more middle school content than high school content. The content in the child-family field was the most, and the main keywords were youth and family. Before COVID-19, a performance evaluation indicated that the proportion of student content was high, whereas after the outbreak of the disease, teacher content increased significantly due to the effect of distance learning. However, compared with video use, the self-expression and participation of users were lower in both periods. The centrality analysis indicated that in the title, 'family' exhibited a high degree of both centrality and eigenvector centrality over the entire period. Degree centrality of the video title was found to be high in the order of class, online, family, management, etc. after the outbreak of COVID-19, and the connection of keywords was strong overall. Eigenvector centrality indicated that career, search, life, and design were influential keywords before COVID-19, while class, youth, online, and development were influential keywords after COVID-19.

• Human Ecology Research
• /
• v.60 no.4
• /
• pp.625-643
• /
• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.28 no.4
• /
• pp.400-410
• /
• 2022

Purpose: This study aimed to understand the keywords and major topics of the educational goals and objectives of nursing educational institutions in South Korea. Methods: From May 10 to May 20, 2022, the educational goals and objectives of all 201 nursing educational institutions in South Korea were collected. Using the NetMiner program, degree and degree centrality, semantic structure, and topic modeling were analyzed. Results: The top keywords and semantic structures of educational goals included 'respect for human (life)-spirit-science-based on, global-competency-professional nurse-nursing personnel-training, professional-science-knowledge-skills, and patients-therapeutic care-relationship.' The educational goals' major topics were clients well-being based on science and respect for human life, a practicing nurse with capabilities and spirit, fostering a nursing personnel with creativity and professionalism, and training of global nurses. The top keywords and semantic structures of the educational objectives included 'holistic care-nursing-research-action-capability, critical thinking-health-problem solving-capability, and efficiency-communication-collaboration-capability.' The educational objectives' major topics were 'nursing professionalism, communication and problem-solving capability; a change of healthcare environments and a progress of nursing practices; fostering professional nurses with creativity and global capability; and clients' health and nursing practice.' Conclusion: Educational goals in nursing presented specific nursing values and concepts, such as respect for human life, therapeutic care relationships, and the promotion of well-being. Educational objectives in nursing presented the competencies of nurses as defined by the Korean Accreditation Board of Nursing Education (KABONE). Recently, the KABONE announced new program outcomes and competencies, which will require the revision of educational goals. To achieve those educational objectives, it is suggested that the expected level of competencies be clearly defined for nursing graduates.

• Journal of Korean Academy of Nursing
• /
• v.53 no.1
• /
• pp.55-68
• /
• 2023

Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

• Journal of dental hygiene science
• /
• v.23 no.4
• /
• pp.302-311
• /
• 2023

Background: Oral health is an important element of well aging. And oral health also affects overall health, mental health, and quality of life. In this study, we sought to identify oral health influencing factors and research trends for well-aging through text analysis of research on well-aging and oral health over the past 12 years. Methods: The research data was analyzed based on English literature published in PubMed from 2012 to 2023. Aging well and oral health were used as search terms, and 115 final papers were selected. Network text analysis included keyword frequency analysis, centrality analysis, and cohesion structure analysis using the Net-Miner 4.0 program. Results: Excluding general characteristics, the most frequent keywords in 115 articles, 520 keywords (Mesh terms) were psychology, dental prosthesis and Alzheimer's disease, Dental caries, cognition, cognitive dysfunction, and bacteria. Research keywords with high degree centrality were Dental caries (0.864), Quality of life (0.833), Tooth loss (0.818), Health status (0.727), and Life expectancy (0.712). As a result of community analysis, it consisted of 4 groups. Group 1 consisted of chewing and nutrition, Group 2 consisted oral diseases, systemic diseases and management, Group 3 consisted oral health and mental health, Group 4 consisted oral frailty symptoms and quality of life. Conclusion: In an aging society, oral dysfunction affects mental health and quality of life. Preventing oral diseases for well-aging can have a positive impact on mental health and quality of life. Therefore, efforts are needed to prevent oral frailty in a super-aging society by developing and educating systematic oral care programs for each life cycle.

• Human Ecology Research
• /
• v.62 no.1
• /
• pp.15-32
• /
• 2024

The purpose of this study was to analyze the research trends and co-author networks of academic articles published in the Journal of the Korean Home Economics Association from 2010 to 2022. The network analysis was conducted using Excel and NetMiner 4.4, and the results were as follows. First, the number of published articles has been maintained at around 40 per year since 2019. By field, most articles were published in the field of child studies and family studies, followed by consumer studies, home management, clothing studies, home economics education, food and nutrition, and housing. The research methods were primarily quantitative (71.61%). Second, the most common keywords in the titles of the published articles were "influence" and "relationship", with "influence", "consumer", "mediating effect", "parent", and "control" identified as influential keywords. Third, the published articles were categorized into nine topics based on subject matter, while the number of topic types varied by year. Fourth, the total number of authors of the 627 articles was 712, with 1.92 authors per article, as well as the number of authors who published two or fewer articles accounted for 85.5% of the total. By institution, Yonsei University had the highest number of authors and the highest number of published articles, while Korea National Open University played a leading role in the network of co-authors by institution. This study is significant in providing basic data for the future development of the Korean Home Economics Association and the field of home economics.