Earticle

이 연구에서는 블록체인 기술의 활용과 관련된 주요 토픽을 분석하기 위해 신문기사에 나타난 ‘블록체 인 기술 활용’ 빅데이터를 토픽 모델링기법을 적용하였다. 이를 위해 2013년부터 2019년까지, 21개의 신문사 로부터 15,617건을 대상으로 토픽을 추출하고 주요 트렌트를 시기별로 구분하여 분석하였다. 분석결과 블록체 인기술 활용과 관련된 기사는 2015년부터 기하급수적으로 증가하였으며 IT_과학 분야와 경제 분야에 집중되 었다. 기간에 따라 차이는 있지만 암호화폐, 비트코인, 가상화폐와 관련된 키워드의 가중치가 높았다. 금융거래 에 집중되었던 블록체인기술은 빅데이터, 사물인터넷, 인공지능으로 점차 확대되었다. 이에 따라 기업의 토픽 변화도 함께 이루어져 금융거래를 위한 은행에서 다양한 분야로 확대되면서 대기업과 글로벌기업으로 집중되었다. 이 연구를 통해 블록체인기술의 활용과 관련한 신문기사의 주요 토픽과 함께 이러한 토픽들이 어떠한 변화 추이를 보이고 있는지에 대해 확인할 수 있었다.

To analyze the main topics related to the use of blockchain technology, the Topic Modeling Technique was applied to the ‘Blockchain Technology Utilization’ big data shown in newspaper articles. To this end, from 2013 to 2019, when newspaper articles on the use of blockchain technology first appeared, the topics were extracted from 21 newspapers and analyzed by time to 15,537 articles. As a result of the analysis, articles related to the utilization of blockchain technology have increased exponentially since 2015 and focused on IT_science and economics. Key words related to cryptocurrency, bitcoin and virtual currency were weighted high, although they differed depending on time. Blockchain technology, which had focused on financial transactions, gradually expanded to big data, Internet of Things and artificial intelligence. As a result, changes in corporate topics were also made together to expand into various fields at banks for financial transactions, focusing on large and global companies. The study showed how these topics were changing, along with the main topics in newspaper articles related to the use of blockchain technology.

본 연구는 중도입국청소년 관련 언론보도 빅데이터에 나타난 주요 키워드와 이 슈를 도출함으로써, 코로나19 시국의 중도입국청소년에 대한 시사점을 제안하기 위해 수행되었다. 이를 위해 한국언론진흥재단에서 운영하는 언론보도 빅데이터 서비스인 빅카인즈를 이용하여 54개 언론매체에서 보도한 775건의 언론보도를 수집하였다. 그리고 네트워크 분석 및 시각화 전문 소프트웨어인 넷마이너로 주 제어 분석과 토픽 분석을 실시하였다. 그 결과, ① 중도입국청소년의 맞춤형 지 원을 위한 대면지도, ② 증가하는 중도입국청소년을 위한 지역사회의 노력, ③ 중도입국청소년을 위한 촘촘한 지원체계 구축 등 3개의 주요 이슈가 도출되었 다. 본 도출결과를 바탕으로 중도입국청소년의 교육적 차원, 정책적 차원에서 시사점을 제안하였다.

This study was conducted to suggest the trend suggestions of migrant youth trends in COVID-19 by deriving key keywords and trends that appeared in media coverage big data related to migrant youth. To this end, Big Caines, a big data service run by the Korea Press Foundation, collected 775 media reports from 54 media outlets. Then, subject word analysis and topic analysis were conducted in NetMinor, a specialized software for network analysis and visualization. The results led to three main trends, ① face-to-face guidance for migrant youth, ② community efforts for increasing migrant youth, and ③ visiting Korean language education for migrant youth. Based on the results of this derivation, suggestions are proposed in terms of educational and policy aspects of migrant youth.

This study aims to identify specific topics, trends, and structural characteristics of scholarly communication research, based on 1,435 articles published from 1970 to 2018 in the Scopus database through Latent Dirichlet Allocation topic modeling, serial analysis, and network analysis. Topic modeling, time series analysis, and network analysis were used to analyze specific topics, trends, and structures, respectively. The results were summarized into three sets as follows. First, the specific topics of scholarly communication research were nineteen in number, including research resource management and research data, and their research proportion is even. Second, as a result of the time series analysis, there are three upward trending topics: Topic 6: Open Access Publishing, Topic 7: Green Open Access, Topic 19: Informal Communication, and two downward trending topics: Topic 11: Researcher Network and Topic 12: Electronic Journal. Third, the network analysis results indicated that high mean profile association topics were related to the institution, and topics with high triangle betweenness centrality, such as Topic 14: Research Resource Management, shared the citation context. Also, through cluster analysis using parallel nearest neighbor clustering, six clusters connected with different concepts were identified.

This study applies topic modeling analysis to AI?dance research articles indexed in SCOPUS and KCI to identify the major themes and knowledge structures of international AI?dance scholarship, and, through comparison with domestic research, to suggest future directions for AI?dance research in Korea. First, while both international and domestic AI?dance research have experienced rapid growth since the pandemic, they exhibit distinct differences in research perspectives and methodologies. International research focuses on the structures, algorithms, and creative systems of AI technologies?such as generative AI, learning, movement modeling, neural networks, and human?computer interaction?thereby conceptualizing dance as a data-driven object of creation and analysis. In other words, AI is understood as a technological medium that performs practical creative functions within dance. In contrast, domestic research largely approaches AI as a tool for education, classroom practice, and applied case studies, remaining at an exploratory stage of potential utilization. AI is positioned not as a component of creative systems but as part of an educational discourse. Consequently, while international research has already entered a convergent and multidisciplinary research ecosystem, domestic research still remains in a foundational stage focused on educational introduction and preliminary application. To bridge this gap, it is necessary to strengthen the core competencies required for AI research, pursue more ambitious and forward-looking studies grounded in a deeper understanding of international scholarship, and expand domestic research into specialized educational topics as well as culturally and identity-based themes.

In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to look for influential topics from scientific article, topic modeling is performed, and then social network analysis is applied to the selected topic models. Abstracts of research papers related to data mining published over the 20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept hierarchies of topic models. Our experimental results show that the subjects of data management and queries are identified in the most interrelated topic among other topics, which is followed by that of recommender systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays an important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.

Purpose: This study examined the knowledge structure and thematic characteristics of health literacy research in Korea through keyword network analysis and topic modeling. Methods: Keyword frequency and co-occurrence network analyses included 637 articles. For latent Dirichlet allocation, English abstracts were combined with standardized author keywords. After preprocessing and document-frequency filtering, 636 articles remained in the effective latent Dirichlet allocation corpus. Models with 3?10 topics were evaluated across 10 random seeds according to log-likelihood, perplexity, semantic coherence, topic distinctiveness, cross-seed stability, and interpretability of representative documents. A five-topic solution was selected, and the final model was estimated using seed 2026. An abstract-only sensitivity analysis was conducted with 633 articles. Results: Health literacy occupied the most central position in the network, as expected because it was the core search concept. Among the remaining keywords, older adults, eHealth literacy, self-efficacy, health behavior, and self-care had the highest degree centrality. Louvain clustering identified six thematic communities. The five latent Dirichlet allocation topics were Health Literacy Measurement, Mental Health Literacy, Health Information and Communication, Digital Health Literacy, and Disease-Specific Health Literacy. Topic prevalence ranged from 17.1% to 21.4%. Disease-Specific Health Literacy was the largest topic (21.4%, n=136), followed by Digital Health Literacy (21.2%, n=135) and Mental Health Literacy (21.1%, n=134). The sensitivity analysis produced a broadly comparable five-topic structure. Conclusion: This study systematically mapped the knowledge structure of Korean health literacy research. The findings shed light on major research themes and their relationships and may help inform future research priorities.

The study reported in this paper aimed to evaluate the topics and opinions of COVID-19 discussion found on Twitter. It performed topic modeling and sentiment analysis of tweets posted during the COVID-19 outbreak, and compared these results over space and time. In addition, by covering a more recent and a longer period of the pandemic timeline, several patterns not previously reported in the literature were revealed. Author-pooled Latent Dirichlet Allocation (LDA) was used to generate twenty topics that discuss different aspects related to the pandemic. Time-series analysis of the distribution of tweets over topics was performed to explore how the discussion on each topic changed over time, and the potential reasons behind the change. In addition, spatial analysis of topics was performed by comparing the percentage of tweets in each topic among top tweeting countries. Afterward, sentiment analysis of tweets was performed at both temporal and spatial levels. Our intention was to analyze how the sentiment differs between countries and in response to certain events. The performance of the topic model was assessed by being compared with other alternative topic modeling techniques. The topic coherence was measured for the different techniques while changing the number of topics. Results showed that the pooling by author before performing LDA significantly improved the produced topic models.

This study explored issues related to the library in the COVID-19 era in YouTube videos in Korea. This study performed social network analysis and topic modeling analysis by collecting 479 YouTube videos, 20,545 words, and 8,379 channels related to COVID-19 and the library from 2019 to 2020. The study results confirmed that YouTube, a social media platform, was used as an important medium to connect users and physical libraries and provide/promote online library services. In the study, major topics and keywords such as quarantine, vlog, and library identity during the COVID-19 pandemic, library services and functions, and introductions and user guides of libraries were derived. Additionally, it was identified that videos about COVID-19 and the library are being produced by various actors (news and media channels, libraries, government agencies, librarians, and individual users). However, the study also identified that the actor network is fragmented through the channel network, showing a low density or weak linkage, and that the centrality of the library in the actor network is weak.

Twitter can spread and share all kinds of information such as facts, opinions, and ideas in real time. In this paper, we empirically compare and analyze the topic categories in Twitter with all top 100 users in each of geographic region. We mainly consider the relationships among regions and selected four regions: Global, Seoul, Tokyo, and Beijing. Each of the top 100 users in Twitter is classified into a specific category and then statistical analysis is conducted. Among eight topic categories, the "Arts" category is the largest and the second is "Life". The correlation between global and Seoul groups has the lowest value among the six pairs of relationships between regional groups, and this difference is statistically significant. We find that the Seoul, Tokyo, and Beijing regional Twitter groups, all in East Asia, have high topical similarity. Based on the correlation analysis, Seoul and Tokyo saliently show a sticky trend. The correlation coefficient presents very a strong positive correlation between Seoul and Tokyo. The correlation between the global group and the East Asian groups is relatively lower than that among the East Asian groups.

This study aimed to identify key topics related to the consumption of second-hand fashion products by analyzing reviews of fashion products on a C2C second-hand trading platform. Reviews of second-hand fashion stores in Bungaejangter were collected and analyzed using text mining and Latent Dirichlet Allocation (LDA), a topic modeling technique. The topic modeling analysis resulted in the extraction of eight topics: customer service and satisfaction, wearing experience and satisfaction, product condition and customer expectations, interaction in the transaction process, gap between expectations and reality, seller friendliness and trust, overall satisfaction, quality and value assessment. The findings of this study provide valuable guidelines for developing strategies to meet customer expectations and demands in C2C-based online secondhand fashion transactions. They will contribute to enabling secondhand fashion sellers and platform operators to deliver customer-centric services and pursue sustainable growth.

Purpose: The purpose of this study was to identify the supportive care needs of parents of preterm children in South Korea using text data from a portal site. Methods: In total, 628 online newspaper articles and 1,966 social network service posts published between January 1 and December 31, 2019 were analyzed. The procedures in this study were conducted in the following order: keyword selection, data collection, morpheme analysis, keyword analysis, and topic modeling. Results: The term "yirundung-yi", which is a native Korean word referring to premature infants, was confirmed to be a useful term for parents. The following four topics were identified as the supportive care needs of parents of preterm children: 1) a vague fear of caring for a baby upon imminent neonatal intensive care unit discharge, 2) real-world difficulties encountered while caring for preterm children, 3) concerns about growth and development problems, and 4) anxiety about possible complications. Conclusion: Supportive care interventions for parents of preterm children should include general parenting methods for babies. A team composed of multidisciplinary experts must support the individual growth and development of preterm children and manage the complications of prematurity using highly accessible media.

Artificial intelligence (AI), an essential skill of the Fourth Industrial Revolution, is being actively taught in higher education; however, AI education is only in the preparatory stage in elementary, middle, and high schools. Investigating various newspaper articles related to AI education to date can aid in basic data collection, which is an important process in the preparatory stage. Accordingly, 13,378 newspaper articles were collected from a total of 21 newspapers, and five topics were extracted using the latent Dirichlet allocation (LDA)-based topic model along with frequency analysis. Newspaper articles from the early 2000s expanded to technologies related to the Fourth Industrial Revolution. Accordingly, education in AI fields should be linked with education in AI-based technology. In addition, efforts should be made to secure the continuity and sequence of AI education in cooperation with related higher institutions and companies.

With the proliferation of generative AI painting technology, “AI painting” has sparked widespread discussion on social media. The Weibo platform, with its large user base and high interactivity, provides an ideal context for exploring public attitudes and topic dynamics. This study combines sentiment analysis and topic modeling to quantitatively analyze 13,379 relevant Weibo posts published between October 2024 and March 2025. For sentiment analysis, we constructed a BERT-based model incorporating a dynamic attention mechanism. After pre-training and fine-tuning on 1,487 domain-specific texts, the model achieved an accuracy of 95.12% and an ROC-AUC of 0.9818. The results reveal a polarized sentiment among Weibo users toward AI painting: 75.9% expressed positive emotions, while 24.1% expressed negative emotions. Positive content tended to combine images and text and received more reposts, whereas negative content was more often original text-only posts but demonstrated a greater capacity to drive interaction, reflecting the "negativity bias" phenomenon. In terms of topic modeling, BERTopic identified five core themes: ethical controversies and industrial impact, technology-driven creation, artistic exploration and aesthetic innovation, digital preservation of cultural heritage, and vertical content operations. These themes highlight the multifaceted significance of AI painting across technological, artistic, and social dimensions. From the perspectives of communication studies and psychology, this study uncovers the emotional structure and communication mechanisms of “AI painting” on Weibo, offering empirical and methodological insights for understanding the social acceptance of generative AI and platform public opinion governance.

본 연구는 지금까지 한국의 영어 교육 분야에서 발표된 챗봇 관련 연구들의 주제 동향을 탐색하고, 시간의 흐름에 따라 연구 주제의 변화를 확인하고자 실시되었다. 본 연구의 목표를 달성하기 위해 학술지 논문을 중심으로 관련 문헌을 선별하여 수집하였다. 그 결과 93편의 학술 논문이 최종 분석 대상으로 선정되었다. 아울러, 분석 대상 논문에서 제목과 초록을 추출하였으며, 이를 바탕으로 토픽 모델링 기법을 활용하여 챗봇 관련 연구들의 주제 동향 및 시간에 따른 주제 변화 추이를 관찰하였다. 본 연구 결과를 정리하면 다음과 같다. 첫째, 한국의 영어 교육 분야에서 실시된 챗봇 관련 연구들은 주로 학생들을 대상으로 하고 있으며, 사용된 챗봇 유형은 당시의 기술 동향이 반영되고 있는 것으로 나타났다. 둘째, 토픽 모델링 분석을 통해 "다양한 목적을 위한 챗봇의 다면적 응용(주제 1)", "챗봇 사용의 효과와 사용자 인식 이해(주제 2)", "챗봇 중심 과업, 과업 별 챗봇 개발 및 학습자-챗봇 상호작용(주제 3)"의 세 가지 연구 주제 동향이 관찰되었다. 셋째, 해당 주제들의 시간적 변화 추이를 살펴본 결과, 주제1의 비중은 시간이 지남에 따라 증가하는 추세를 보였고, 주제2는 반대로 감소하는 추세를 보였다. 주제3에서는 시간의 흐름에 상관없이 일관되게 낮은 관찰 빈도가 목격되었다. 본 연구는 한국의 영어 교육 맥락을 바탕으로 수행되었으나, 한국을 포함하여 유사한 외국어 학습 배경을 공유하는 학문 공동체에 역시 영어 교육에서의 효과적인 챗봇 통합을 위해 필요한 향후 연구 주제에 대해 다양한 방향과 지침을 제공해줄 수 있다.

This study aimed to investigate the specific topics of scholarly inquiries related to a chatbot within the domain of English pedagogy in South Korea and to track the changes in these topics over time. To achieve this goal, rigorous criteria were established to screen and gather relevant literature, resulting in the selection of 93 scholarly articles. From these articles, titles and abstracts were extracted and analyzed using topic modeling techniques. The findings of this investigation are as follows: first, it was observed that most chatbot-related scholarly articles in Korea predominantly involved students as subjects, and the types of chatbots used reflect sensitivity to current technological trends. Secondly, the topic modeling analysis identified three main topics: "the multifaceted application of chatbots for various purposes (Topic 1)," "understanding the impacts and perceptions of chatbot use (Topic 2)," and "chatbot-centric tasks, the development of task-specific chatbots, and learner-chatbot interactions (Topic 3)." Lastly, the temporal trajectory of these topics revealed a noticeable increase in the proportion of Topic 1, a decrease in Topic 2, and a consistently low frequency of Topic 3. Despite being limited to the context of South Korean English pedagogy, these insights provide several academic implications. Specifically, the findings can help researchers better understand and identify future research areas within and beyond the South Korean English education context.

본 수영은 대중적인 생활체육으로 인식되고 있다. 하지만 아파트, 호텔 등에 설치된 실내수영장에서 종종익수사고가 발생하고 있다. 따라서 본 연구에서는 쇼셜미디어, 뉴스, 잡지, 학술논문에서 수집한 데이터를 대상으로 텍스트마이닝 기법을 활용하여 수영장 익수사고를 중심으로 키워드 분석을 수행하였다. 이를 통해 수영장에서 발생하는 사고의 원인, 결과와 밀접한 키워드를 분석하여 향후 사고예방을 위한 기술적 연구에 활용하는데 연구의 목적이 있다. 본 연구에서는 최근 5년간 소셜 미디어 동영상(102개)과 뉴스기사(30개)를 활용하여 키워드 및 토픽 모델링 분석을 수행하였다. 그 결과 첫째, 아동 익수 사고와 구조 활동, 둘째로 부주의와 그에 따른 법적 책임, 셋째, 수영장, 워터파크, 주거시설 내 수영시설에서의 아동 안전사고 위험으로 분류되었다. 네트워크 분석 결과는 ‘수영활동’과 ‘조사’와 같은 키워드가 매개 역할을 하는 것으로 나타났다. 본 연구 결과는 향후 수영장 익수사고의 인과관계 분석, 기술적 해결방안을 마련하는 기초자료로 활용성이 높을 것으로 판단된다.

Swimming is widely recognized as a popular recreational sport. However, drowning accidents frequently occur in indoor swimming pools located in apartments, hotels, and other facilities. Therefore, this study employs text mining techniques to analyze drowning accidents in swimming pools using data collected from social media, news articles, magazines, and academic papers. The purpose of the study is to identify keywords closely related to the causes and consequences of these accidents and to provide insights that can inform future technological research aimed at accident prevention. This study utilizes 102 social media videos and 30 news articles from the past five years to conduct keyword analysis and topic modeling. The results reveal three main themes: (1) child drowning accidents and rescue activities, (2) negligence and related legal responsibilities, and (3) child safety risks in swimming pools, water parks, and residential swimming facilities. Network analysis further indicates that keywords such as “water activity” and “investigation” serve as mediating nodes within the keyword network. These findings are expected to serve as valuable foundational data for future causal analyses of swimming pool drowning accidents and for developing technical solutions to enhance safety in swimming environments.

본 연구의 목적은 토픽모델링 분석법을 통해 국내 트라우마 연구의 동향을 분석을 통해 주요 연구주제를 확인하고 성장 또는 쇠퇴하는 주제들을 규명하고자 하였다. 이를 위해 1996년부터 2016년까지 발간된 논문들 중 199편의 논문들을 선별하여 토픽모델링 분석을 실시하였다. 그 결과 총 39개의 주제가 추출되었고, 이는 다시 15개의 중범위 주제와 5개의 상범위 주제로 범주화되었다. 트라우마 연구논문의 수는 2008년, 2011년, 2014년을 기점으로 전반적인 증가 추세를 보임이 확인되었다. 특히, 2014년-2016년 논문 성과물이 전체 논문 실적의 55.78%였는데, 이는 ‘세월호 침몰’ 재난의 영향에 의한 것임을 확인하였다. 성장속도가 높은 주제는 ‘다양한 유형의 트라우마 사건에 따른 주요 심리적 증상들’과 ‘트라우마에 대한 심리사회적 개입’에 대한 주제였으며, 성장속도가 상대적으로 낮은 주제는 ‘PTSD의 생리심리사회적 기제’와 ‘트라우마 내러티브’에 대한 것이었다. 마지막으로, 본 연구의 주요 결과에 따른 논의에서 본 연구의 제한점과 함께 추후 트라우마 연구의 방향에 대해 제언하였다.

The purpose of this study is to analyze research trends related to psychological trauma in South Korea to identify upward and downward topics. A topic modeling analysis was conducted for 199 articles published from 1996 to 2016. The number of articles on trauma increased in 2008, 2011, and 2014 and those published between 2014 and 2016 covered 55.78% of the total because of Sewol ferry disaster. Based on the topic modeling analysis, 39 key topics were identified and categorized into 15 mid-range topics and 5 high-range topics. Hot topics were found to be ‘major psychological symptoms following various types of traumatic events’ and ‘psycho-social intervention for trauma’, while cold topics were ‘biopsychosocial mechanism of PTSD’ and ‘trauma narrative’. Implications and limitations of this study were discussed based on the results, along with the directions for future research on trauma.

Hong Kong cinema has undergone significant changes, shifting from an actor-driven, story-rich industry to one influenced by large-scale productions and global trends. This study applies topic modeling to analyze audience reviews of Hong Kong films from two periods: pre-2000 and post-2000. Using Latent Dirichlet Allocation (LDA), it identifies key thematic changes, highlighting a decline in acting quality and storytelling depth. Findings indicate that pre-2000 films were praised for strong performances and engaging narratives, while post-2000 films prioritized large-scale productions and high-budget visuals but faced criticism for weaker storytelling and predictable plots. The study suggests that Hong Kong cinema has struggled to retain its distinct identity amid globalization and competition from Hollywood and mainland Chinese productions. To regain its prominence, a balance between high production values and compelling storytelling is necessary. Future research should explore cross-cultural comparisons and industry perspectives to further understand audience engagement trends.

This study analyzes Korean and Chinese consumer perceptions of Hanbang (traditional Korean herbal) cosmetics, specifically focusing on Sulwhasoo’s Jaum two-piece set. Using topic modeling, 7,000 consumer reviews from Naver (Korea) and Baidu (China) were examined to uncover the key themes that influence consumer satisfaction and dissatisfaction. The results reveal significant similarities and differences between the two markets. In both countries, the product is frequently purchased as a gift, and price sensitivity is a major concern. However, Korean consumers prioritize delivery experiences and product functionality, while Chinese consumers focus more on product quality and effectiveness. These findings highlight the need for targeted strategies in each market. For success in Korea, competitive pricing and improved logistics are crucial, whereas in China, maintaining high product quality and capitalizing on the gifting culture are essential. Additionally, global expansion requires educating consumers on the benefits of Hanbang cosmetics, ensuring product consistency, and adapting to regional preferences. This research provides valuable insights for cosmetic companies aiming to enhance their market presence both locally and internationally.

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.

This present study aims to explore the English language needs of 37 students at a university in Korea. Specifically, it delves into their specific workplace communication needs through a questionnaire survey centered on a work-integrated learning (WIL) module, pertaining to: 1) the target situation, 2) workplace discourse, 3) their current language skills and lacks, and 4) effective ways of learning workplace communication. Using a combination of quantitative and qualitative methods, survey data were obtained through responses to 17 questionnaire questions. Findings show that while the Korean university students generally agree that they need communication skills in English in workplaces, they have difficulties in using interpersonal functions. They also indicate that while they are aware of the influence of power relations between interlocutors and differences in communication style, they are not aware of the need to learn proactively or autonomously in the workplace. The pedagogical implications suggest collaborative efforts among business English curriculum designers and ESP practitioners to exchange experiences, explore interdisciplinary approaches, and tailor courses for diverse topics.