Earticle

본 연구는 간호창업 관련 문헌에서 나타난 간호창업의 관심 주제 및 간호창업 경험의 속성, 간호창업의 방향성을 탐색하기 위해 시행되었다. MEDLINE, Embase, Cochrane Library DB를 통해 55편의 간호창업 관련 문헌을 선정하 여 덱스트 네트워크 분석 방법을 적용하여 분석하였다. 분석결과 단순출현 빈도와 연결중심성에서 공통적인 핵심키워드는 ‘business’, ‘care’, ‘nursing’, ‘healthcare’, ‘service’으로 나타났다. 연결중심성에서 높은 순위를 보이는 키워드는 ‘mission’, ‘vision’, ‘team’으로 나타났다. 이에 본 연구결과가 체계적인 간호창업 교육프로그램과 간호창업 이론 개발 의 기초 자료로 활용 될 수 있을 것이다.

The purpose of this study is to explore text data of nursing start-up. 55 literatures were extracted from MEDLINE, Embase and Cochrane Library Data BASE. Text network analysis applied by using python network program. Key words with highest frequency and degree centrality were ‘business’, ‘care’, ‘nursing’, ‘healthcare’, ‘service’. Keywords with highest degree centrality were ‘mission’, ‘vision’, ‘team’. Based on the results nursing entrepreneurship support should be provided to develop competitive nursing services reflecting the specificity and science of nursing, to strengthen business competencies essential for nursing entrepreneurship, to expand nursing expertise and to present role models. The result will serve a basement to development systematic educational program and theory in nursing start-up.

본 논문은 고령운전자의 운전 면허증 자진 반납을 유도하기 위하여 운전 시뮬레이터를 활용한 안 전 운전 수행능력 평가 콘텐츠를 제안하고자 한다. 사용빈도가 많은 학술 데이터베이스에서 운전 시뮬레이터를 사용한 고령운전자 대상의 운전 능력을 평가하는 관련 문헌들을 검색한 후, 텍스트 네트워크를 사용하여 주행에 관련된 키워드들 간 연결중심성, 근접중심성, 매개중심성을 분석하였 다. 이에 대한 결과로 도로유형 별 주행에서는 2차선과 4차선의 고속도로에서 연결중심성, 고속도 로와 교차로에서는 매개중심성이 높게 나타났다. 그리고 주행상황 별 평가내용 키워드 분석 결과 에서는 연결중심성이 ‘좌회전’, ‘무단횡단’, ‘정체된 차량 회피’ 등에서 높게 나타났으며, 매개중심성에 서는 ‘좌회전’이 가장 많이 사용되는 평가내용으로 나타났다. 하지만 도로유형별 주행상황과 이에 대한 평가내용에 대한 근접중심성이 매우 유사하게 나타난 것은 각각의 실험 설계가 서로 비슷하 다는 것을 의미한다.

The present paper intended to propose contents for evaluating the driving performance using a driving simulator to induce the voluntary return of driver's license by elderly drivers. Research papers related to evaluating the driving performance of elderly drivers using a driving simulator were collected from a frequently used academic database and analyzed to obtain degree, closeness, and betweenness centrality indices of inter-keywords related to driving by using a text network. As a result, the road types that showed the highest degree centrality index were 2-lanes and 4-lanes highway, and the highest betweenness centrality index were highway and intersection. In addition, the driving situations that showed the highest degree centrality index were "turning left ", "jaywalking" and "avoid stalled car", and the highest betweenness centrality index were "turning left"(i.e. turning left was the most frequently used evaluation contents). However, the closeness centrality index of the driving situation by road type and the evaluation contents showed similar results. It means that each experimental design is similar.

본 연구는 간호사의 직무 스트레스와 자기효능감과의 관계를 규명하기 위하여 관련 연구의 동향을 고찰하고 텍스트 네트워크 분석을 시행하였다. 선행문헌고찰을 위하여 국내 3곳, 국외 1곳의 데이터베이스를 이용하여 ‘간호 사’, ‘스트레스’, ‘자기효능감’, ‘nurse', ‘stress', ‘self-efficacy’를 주요 검색어로 검색하였다. 총 18편의 논문이 대상 문헌으로 선정되었다. 이중 9편의 연구에서 간호사의 직무 스트레스와 자기효능감 간에 통계적으로 유의한 음의 상관관계가 있음을 보고하였다. 그러나 도구의 선택에 있어 번안자에 따라 문항을 선택적으로 사용하여 상이한 결 과가 도출되어 동일한 도구를 사용한 다른 논문들과의 비교 분석이 어려웠다. 또한, 18편 논문의 초록에서 키워드를 추출하여 텍스트 네트워크 분석을 시행하였다. 출현 빈도수가 가장 높은 단어는 직무스트레스였고, 이를 기준으로 관계를 분석하였을 때 출현 빈도수가 높은 주요어는 자기효능감, 의료기관, 상관성이었다. 해당 주요어간의 관계를 명확하게 하기 위해 한국형 도구 개발을 통한 영향요인 탐색 연구 수행을 제언한다.

This study performed to identify the relationship between job stress and self-efficacy based on the related research review and text network analysis. For the literature review, we performed the search process at three domestic and one foreign database using key words, ‘nurse', ‘stress', ‘self-efficacy’. A total of 18 papers were selected as the target literature. Nine of these studies reported a statistically significant negative correlation between nurses' job stress and self-efficacy. It was difficult to compare between studies' results because of the optional usage of the questionnaires. In addition, a text network analysis was conducted by extracting keywords from the 18 papers. The keyword with the highest frequency of appearance was job stress, and the main words with high frequency of emergence were self-efficacy, hospital, and correlation. To clarify the relationship between the keywords, it is proposed to perform a survey on the influence factors through the development of Korean version measurement.

Purpose: The purpose of this study was to analyze students experiences during clinical practice in nursing management. Methods: Assessing through computerized databases, self-reflection reports of 57 students were analyzed. Text network analysis was applied to examine the research. The keywords from each student's reports were extracted by using the programs, KrKwic and NetMiner. Results: The results of the keyword network analysis of what students learned in the nursing process included 27 words. The keyword network analysis of what students learned from the problem solving process included 23 words and the keyword network analysis of improvements in Clinical Practice of Nursing included 31 words. Conclusion: Studies related to clinical practice have been increasing, and themes of the studies have also become broader. Further research is required to investigate factors affecting clinical practice specifically in nursing management. Further comparative studies are necessary to define differences in clinical practice systems related to improving nursing students competency.

Purpose: This study aimed to analyze the experiences of new nurses during their first year of hospital employment to gather data for the development of an evidence-based new nurse residency program focused on adaptability. Methods: This study was conducted at a tertiary hospital in Korea between March and August 2021 with 80 new nurses who wrote in critical reflective journals during their first year of work. NetMiner 4.5.0 was used to conduct a text network analysis of the critical reflective journals to uncover core keywords and topics across three periods. Results: In the journals, over time, degree centrality emerged as "study" and "patient understanding" for 1 to 3 months, "insufficient" and "stress" for 4 to 6 months, and "handover" and "preparation" for 7 to 12 months. Major sub-themes at 1 to 3 months were: "rounds," "intravenous-cannulation," "medical device," and "patient understanding"; at 4 to 6 months they were "admission," "discharge," "oxygen therapy," and "disease"; and at 7 to 12 months they were "burden," "independence," and "solution." Conclusion: These results provide valuable insights into the challenges and experiences encountered by new nurses during different stages of their field adaptation process. This information may highlight the best nurse leadership methods for improving institutional education and supporting new nurses' transitions to the hospital work environment.

Purpose: This study quantitatively examined the portrayal of physician assistant (PA) nurses in Korean media by integrating text network analysis with latent Dirichlet allocation (LDA) topic modeling. Methods: A total of 3,564 news articles published by nine major Korean media outlets between 2020 and 2024 were analyzed. Content analysis was conducted using term frequency-inverse document frequency calculations, network centrality analysis, and LDA topic modeling to extract key terms, map discourse structures, and identify latent topics. Results: The analysis identified four primary topics in Korean media discourse: "healthcare workforce expansion policies" (30.4%), "hospital clinical practice and operational management" (23.5%), "institutionalization of the PA nursing role" (17.8%), and "COVID-19 response and public health crisis management" (28.3%). High-centrality keywords included "hospital," "medical," "patient," "physician," "government," and "nurse," indicating that the discourse primarily focused on clinical settings. Topic modeling revealed a major shift from pandemic-centered coverage in 2020 to a focus on healthcare workforce policy and PA nurse institutionalization in 2024, coinciding with the passage of the Nursing Act. Conclusion: This study provides empirical evidence suggesting that the portrayal of PA nurses in Korean media discourse evolved from a peripheral regulatory issue to a central healthcare delivery solution, particularly in the contexts of workforce management, clinical practice, and crisis response. Our findings suggest that PA nurse institutionalization received broader attention when positioned as part of systemic healthcare improvements addressing concrete clinical needs. These results offer valuable insights for policymakers and administrators in framing and implementing workforce policy reforms.

Purpose: This study aimed to identify the main keywords and research topics used in research on problematic smartphone use (PSU) among children (6-12 years old), including parental factors. Methods: The publication period for the literature was set from January 2007 to January 2022, as smartphones were first released in 2007. In total, 395 articles were identified, 230 of which were included in the final analysis. Text network analysis was performed using NetMiner 4.5. Results: Research on this topic has steadily increased since 2007, with 40 papers published in 2021. Eight main research topics were derived: group 1, parental attitudes; group 2, children's PSU behavior and parental support; group 3, family environment and behavioral addiction; group 4, social relationships; group 5, seeking solutions; group 6, parent-child relationships; group 7, children's mental health and school adaptation; and group 8, PSU in adolescents. Conclusion: Parental factors related to PSU have been studied in various aspects. However, more active research on school-age children's PSU needs to be conducted due to the paucity of research in this population compared to studies conducted among adolescents. The results of this study provide useful data for selecting research topics in the field of PSU.

Purpose: This study aimed to explore the perspectives of frontline nurses working during the novel coronavirus disease 2019 (COVID-19) pandemic. Methods: An online qualitative study was conducted using a pragmatic approach. The data were collected in August 2021. Registered Korean nurses who provided direct nursing care to patients with confirmed COVID-19 were eligible for this study. An online survey was used to gather free-text data, which were then analyzed using machine-based network analysis and summative content analysis. Results: The analysis examined the responses of 126 participants and led to the identification of six prominent themes. These themes were further classified into three distinct levels: personal, task, and organizational. The identified themes are as follows: "collapse of personal life," "being overwhelmed by the numerous roles required," "personal protective equipment was sufficiently provided, but that is not enough," "changes in interprofessional collaboration," "inappropriate workforce management," and "diverted allocation of healthcare services and resources." Conclusion: Our findings highlight areas for improvement in resources, systems, and policies to enhance preparedness for future pandemics.

Purpose: The purpose of this study is to identify the knowledge structure of the concept of resilience of dementia caregivers and to understand core topics and the trends in dementia caregiver resilience research over time. Methods: This study is a quantitative content analysis using text network analysis on the studies on resilience of caregivers of people with dementia. We searched the available literature in two scientific databases, PubMed and the Web of Science. Term frequency, the number of co-occurrence, and centrality were analyzed, and Latent Dirichlet Allocation (LDA) analysis was conducted with Net Miner program. Results: A total 187 articles were identified. The core keywords found were "care", "family", "support", "burden", "intervention", "health", "stress", "relationship", "symptom", "depression", "partner", and "quality of life". Four main topics were identified through the LDA analysis, such as "support from family and social resources", "hardships to overcome", "building strength to overcome adversity", and "coping with negative emotions". There was only one study before 2000, but the number of studies steadily increased to 50 for the years 2011-2015, and 111 for the years 2016-2020. Conclusion: This is the first study to analyze the knowledge structure and research trend of resilience research of caregivers of people with dementia through social network analysis and topic modeling. This study provides a scientific basis of the future studies in Korea. Moreover, it is meaningful in that it brought a comprehensive understanding of the studies by comprehensively analyzing the studies of the resilience of caregivers of people with dementia.

Purpose: This study analyzed research trends in infant and toddler rearing behavior among family caregivers over a 10-year period (2010-2021). Methods: Text network analysis and topic modeling were employed on data collected from relevant papers, following the extraction and refinement of semantic morphemes. A semantic-centered network was constructed by extracting words from 2,613 English-language abstracts. Data analysis was performed using NetMiner 4.5.0. Results: Frequency analysis, degree centrality, and eigenvector centrality all revealed the terms ''scale," ''program," and ''education" among the top 10 keywords associated with infant and toddler rearing behaviors among family caregivers. The keywords extracted from the analysis were divided into two clusters through cohesion analysis. Additionally, they were classified into two topic groups using topic modeling: "program and evaluation" (64.37%) and "caregivers' role and competency in child development" (35.63%). Conclusion: The roles and competencies of family caregivers are essential for the development of infants and toddlers. Intervention programs and evaluations are necessary to improve rearing behaviors. Future research should determine the role of nurses in supporting family caregivers. Additionally, it should facilitate the development of nursing strategies and intervention programs to promote positive rearing practices.

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

Purpose: This study aimed to explore the public discourse surrounding care robots and identify theirpotential social values. Methods: Data related to care robots were collected from major Korean portal sites and social mediabetween January 1, 2021, and December 31, 2023, using TEXTOM. Core keywords were extractedthrough text mining. Topic modeling and semantic network analysis using UCINET 6.0 were conductedto examine keyword associations and network structure. An analytic framework based on thematicintegration was applied to synthesize the findings and derive social value domains. Results: A total of 9724 keywords were extracted from 21,216 documents. High-frequency and centralkeywords included older adult, companion robot, business, service, and artificial intelligence. Topicmodeling identified four major topics: (1) companion robots supporting care to bridge the care gap, (2)care robots leading new economic trends, (3) care robots supporting human care across various sectors,and (4) government-led expansion of social care through care robots. Eight clusters were identifiedthrough semantic network analysis. By applying the analytic framework, these clusters were systematically integrated with topic modeling results, and the social values associated with care robots werecategorized into four domains: health, labor, economy, and innovation. Conclusion: Care robots have the potential to go beyond functional support by addressing broader socialchallenges and contributing to care innovation. However, current discourse remains provider drivenwith limited public engagement. There is a need to enhance communication of care robots’ social valueand promote user-centered approaches in nursing and care practice.

This study aimed to identify latent clusters underlying suicide phenomena in South Korea from 2011 to 2020, a period marked by the country's highest suicide rates. To achieve this, 12,570 news articles were collected from BIG KINDS, a news article database of the Korea Press Foundation, and analyzed using big data techniques. Text mining was applied to article titles using Textom, followed by CONCOR analysis in UCINET6. Results were visualized using NetDraw. Through frequency analysis, 7,542 keywords were extracted. Of them, 86 high-frequency keywords were selected for network analysis. The CONCOR analysis revealed seven key thematic clusters: school, public officials, military, family, anomie, suicide attempts, and suicide locations. This study contributes to a deeper understanding of interconnected socio-cultural factors influencing suicide dynamics in South Korea. By examining a large, diverse dataset over a ten-year period, this research offers new insights into the evolution of suicide-related discourse and the role of media in shaping public attitudes. Findings of this study provide valuable implications for suicide prevention strategies, policy-making, and future research on the role of media in shaping societal perceptions of suicide.

This study investigates how digital discourse surrounding Yuseong-gu public libraries is structured and how it informs evidence-based policy architecture. Online text data were collected from major Korean portals (Naver and Daum) between July 2022 and June 2025 using "Yuseong-gu public libraries" as the core search query. The corpus was analyzed using text mining, keyword network analysis, and latent Dirichlet allocation (LDA) topic modeling. Word frequency and TF-IDF results indicate that place-anchored identifiers (e.g., Yuseong-gu, Daejeon) and culture-related vocabulary constitute the discourse backbone, while managerial and operational terms such as integration, support, and homepage signal demand for coordinated governance and enhanced digital accessibility. N-gram analysis further emphasizes the demand for an integrated information and participation portal, most clearly reflected in the recurrent sequence "Yuseong-gu-integrated-library-homepage." Network analysis reveals a high-density structure with a short average path length, confirming strong thematic interconnectedness; the node "library" functions as the primary hub and is directly linked to "culture," indicating the library's discursive positioning as a cultural platform. The findings support strategic policy directions, including a hub-satellite spatial system embedded across neighborhood life zones, cross-sectional programming integrating education, culture, and community participation, a mobile-first integrated digital portal, and institutionalized partnerships with schools and local cultural institutions.

For the autoencoder (AE) implemented as a construction component, this paper uses the method of greedy layer-by-layer pre-training without supervision to construct the stacked autoencoder (SAE) to extract the abstract features of the original input data, which is regarded as the input of the logistic regression (LR) model, after which the click-through rate (CTR) of the user to the advertisement under the contextual environment can be obtained. These experiments show that, compared with the usual logistic regression model and support vector regression model used in the field of predicting the advertising CTR in the industry, the SAE-LR model has a relatively large promotion in the AUC value. Based on the improvement of accuracy of advertising CTR prediction, the enterprises can accurately understand and have cognition for the needs of their customers, which promotes the multi-path development with high efficiency and low cost under the condition of internet finance.

This study utilized big data to understand consumers' perceptions of bakery cafes, which have become a post-COVID-19 consumer trend This study utilized big data to understand consumers' perceptions of bakery cafes, which have become a post-COVID-19 consumer trend. By doing so, aimed to use it as a basis for understanding the meaning of consumer behavior using bakery cafes. To do so, we collected titles and original posts containing the word "bakery cafe" from web pages, blogs, and cafes on Naver, Daum, Google, and Twitter from January 1, 2020 to July 4, 2023. Through the Textom and UCINET 6.0 to frequency analysis, identify structural characteristics of the network, key word centrality analysis, and CONCOR(CONvergence of iterated CORrelation) analysis. The results of this study are as follows: First, The top 10 words with the highest frequency of appearance are ‘bakery cafe’, ‘cafe', ‘bakery', ‘bakeies', ‘big store', ‘recommended', ‘matzip', ‘coffee', ‘variety', ‘varieties'. Second, The degree centrality analysis results show that the top 10 words are ‘bakery cafe’, ‘cafe', ‘bakery', ‘big store', ‘recommended', ‘matzip', ‘coffee', ‘variety', ‘varieties', ‘dessert’. So overall, higher frequency and higher importance words indicate that they are more central to the connection. Through a CONCOR analysis, keywords were divided into three clusters. Each cluster was named as ' bakery cafe image, 'bakery cafe usage status', and 'bakery cafe selection properties'. And specially, Each cluster contains a neighborhood associated with a bakery cafe. Therefore, it is necessary to establish a marketing strategy for bakery cafes based on the results of this study.

본 연구는 최근 30년간 발표된 소아완화간호 연구를 대상으로 텍스트 네트워크 분석과 토픽 모델링을 적용하여 연구의 지식 구조와 주제적 동향을 규명하고자 하였다. PubMed, Embase, CINAHL 데이터베이 스에 등재된 소아완화분야 간호 관련 학술 논문의 초록을 분석 대상으로 하였고, 핵심 주요어의 빈도, 연결중심성, 매개중심성을 분석하였다. 토픽 모델링을 적용하였다. 총 1,252편의 초록이 최종 분석에 포함되었다. 핵심 주요어는 ‘가족’, ‘부모’, ‘요구’, ‘생애말기’, ‘경험’으로 나타났다. 토픽모델링 분석 결과, ‘생애말기 돌봄’, ‘가족의 요구 지지’, ‘서비스 전달체계’, ‘증상 관리’의 네 가지 주요 연구 주제가 도출되었다. 시기별 분석에서는 소아완화간호 연구의 꾸준한 양적 증가와 함께, 가족의 요구 지지와 생애말기 돌봄과 관련된 연구가 뚜렷하게 증가하는 경향을 보였다. 본 연구 결과는 소아완화간호 연구가 증상 중심 중재에서 가족 중심 돌봄과 생애말기 돌봄을 강조하는 전인적 돌봄으로 심화하고 있음을 보여준다. 이는 향후 소아완화간호 실무, 교육 및 연구 영역에서 가족 중심 역량과 생애말기 돌봄 역량을 강화할 필요성을 시사한다.

This study aimed to examine the knowledge structure and research topic trends in pediatric palliative nursing by applying text network analysis and topic modeling to published articles over the past 30 years. Data were extracted from abstracts of peer-reviewed articles related to pediatric palliative nursing published between 1995 and 2025. Articles were retrieved from PubMed, Embase, and CINAHL. Keyword frequency, degree centrality, and betweenness centrality were analyzed. Latent Dirichlet Allocation topic modeling was applied. A total of 1,252 abstracts were analyzed. Topic modeling revealed four major research topics: ‘end-of-life care’, ‘supporting family needs’, ‘service delivery system’, and ‘symptom control’. The findings demonstrate that pediatric palliative nursing research has evolved from a primarily symptom-focused approach toward holistic care that emphasize family-centered care and end-of-life care. These results highlight the importance of strengthening family-centered competencies and end-of-life care competencies in pediatric palliative nursing practice, education, and research.

Since the government issued ICT priority policy for the last decade, Vietnam was reported to have impressed development of ICT infrastructure and Internet users. Until May of 2015, Vietnam has 39.7 millions of Internet users and 31 millions of social network user accounts. Facebook is the dominant website with more than 22 million Vietnamese users and 70% of those accesses Facebook via mobile phone. Several companies have utilized Facebook as the most effective interaction channels. The increasing of big text data such as posts and comments on Facebook that embed customer opinions requires method to mine sentiment text in Vietnamese language. This research applies machine learning with several algorithms such Naive-Bayes, decision trees and Support Vector Machine (SVM) for Vietnamese text data collected from fast-food industry on Facebook. The experiment results show that machine learning methods are able to classify Vietnamese sentiment text with the accuracy over 70%. Thus we proposed several recommendations for mining Vietnamese social text data.

After the 4th industrial revolution, smart logistics has become a hot issue, and scholars in logistics, engineering, transportation, and management are all being led by these changes. There have been an increasing number of studies on smart logistics in recent years, but the research fields of smart logistics are extensive, and it is not easy to distinguish the correlation between research directions and industries. Furthermore, smart logistics is a complex issue, and research on research trends is lacking. Therefore, to fill this research gap, this study aims to identify smart logistics research trends based on text mining and network analysis techniques using various software such as R, UCINET, and Power BI under the background of big data. Using these big data techniques, it is possible to grasp and analyze smart logistics research trends more objectively and generally. In addition, it is expected to be a good reference for future research.

ESG management is essential to ensure the sustainability of companies in an environment where many risks such as climate change are impacting. It is essential to promote academic research that reflects social requirements so that ESG management can be implemented in all fields of our society. The purpose of this research is to identify social issues and trends linked to ESG and determine whether academic studies are being performed to address them. The study aimed to identify social issues and trends related to ESG, news article texts from Big Kinds, a news search site, were collected and topic modeling and semantic network-based text analysis were performed. In order to understand academic research topics related to ESG, summary texts of ESG-related academic papers were collected on the RISS site, and topic modeling and semantic network-based text analysis were performed. The article confirms an increasing interest in ESG from both social and academic perspectives. However, there is a slight disparity between the two, with social trends related to ESG management gaining attention from larger companies and then spreading to small and medium-sized enterprises, while academic research on ESG tends to focus mainly on the concept and principles.