Earticle

In information systems (IS), Information Technology Service Management (ITSM) has become a popular research area among IS and management researchers as a result of industry push and the development and advancement of research in service sciences. From academic perspective, a growing number of papers have been published addressing many aspects of ITSM issues. This paper presents the results based on a study of comprehensive review of publications in ITSM over the past decade (from 2000 to 2010) with the objective of answering the following research question: what is the current state of research in ITSM, from theoretical foundation and research topic perspectives. Our findings suggest that 1) there is generally a lack of theoretically driven researches; 2) the field is still developing with a growing number of published papers dealing with the development of concepts, constructs, models, methods and implementations for theory development; 3) ITSM performance issues, justifications, and IT Infrastructure Library (ITIL) topics are among the most popular topics of research.

본 논문의 연구목표는 LDA(Latent Dirichlet Allocation) 모델을 적용하여 국가연구개발사업을 통해 수행되고 있는 ICT(Information and Communication Technology) 분야의 연구과제에 대한 주요 연구 토픽과 동향을 탐색 하는데 있다. 연구방법에는 NTIS(National Science and Technology Information Service)로부터 최근 5년간 국가 연구개발사업의 전체 연구과제 정보를 다운로드받고 이를 정보통신기획평가원(IITP)의 EZone 시스템과 매칭하여 ICT 분야 연구과제 5,200건을 확보하고, 토픽모델링 기법중 하나인 LDA 모델을 적용하여 연구토픽과 연구동향을 조사하였 다. 실험결과로, ICT분야 연구과제에 대한 연구토픽은 인공지능, 빅데이터, 사물인터넷(Internet of Things)과 같은 지능정보기술로 확인되었고 연구동향에는 초실감미디어에 관한 연구가 활발히 진행되고 있음을 확인하였다. 끝으로 본 논문에서 진행된 국가연구개발사업에 대한 토픽모델링 결과는 향후 ICT분야 연구개발 계획 및 전략수립, 정책, 과제기 획 등 중요한 정보로 활용될 수 있을 것이다.

The research objectives investigates main research topics and trends in the information and communication technology(ICT) field, Korea using LDA(Latent Dirichlet Allocation), one of the topic modeling techniques. The experimental dataset of ICT research and development(R&D) project of 5,200 was acquired through matching with the EZone system of IITP after downloading R&D project dataset from NTIS(National Science and Technology Information Service) during recent five years. Consequently, our finding was that the majority research topics were found as intelligent information technologies such as AI, big data, and IoT, and the main research trends was hyper realistic media. Finally, it is expected that the research results of topic modeling on the national R&D foundation dataset become the powerful information about establishment of planning and strategy of future’s research and development in the ICT field.

본 연구는 2007년부터 2025년까지 다문화가족지원센터 관련 논문 202편의 연구동 향을 분석하였다. 다문화가족지원센터 관련 논문을 연도별, 종류별, 유형별, 연구대상 별, 주제 영역별로 연구동향을 파악하기 위한 연구문제를 설정하고, 기초 통계치로 결 과를 제시하였다. 첫째, 2007년부터 2023년까지 꾸준히 논문이 발표되었으나, 다문화 가족지원센터라는 명칭을 사용하지 않고 가족센터로 명칭이 통합된 이후 2024년부터 는 다문화가족지원센터라는 제목이나 주제어로 논문이 발표되지 않았다. 둘째, 학술지 에 게재한 논문의 편수가 학위논문보다 2배 이상 많았으며, 학술지 논문 중 한국연구재 단 등재지에 발표된 논문이 대부분이었다. 셋째, 질적 연구나 문헌 연구에 비해 양적 연구가 많이 수행되기는 하였으나, 양적 연구와 문헌 연구는 급격하게 감소하는 추세 인 반면 질적 연구가 급격하게 증가하였다. 넷째, 연구대상은 인적 대상 중 다문화가족 지원센터 종사자가 절반 이상이었으며 이에 비해 센터 이용자/참여자와 다문화가족은 적은 수에 불과했다. 다섯째, 연구주제 영역은 다문화가족지원센터의 운영관리에 관한 연구가 가장 많았으며, 다음으로 인사관리에 관한 연구가 차지했다. 이에 비해 기본프 로그램이나 별도지원사업에 관한 연구는 그리 많지 않았다. 본 연구를 통해 다문화가 족지원센터에 대한 이해를 높이고 앞으로 어떠한 연구가 앞으로 필요한 지 추후 연구 방향을 제시하였다.

The purpose of this study was to analyze research trends regarding multicultural family support centers by year, research type, data collection method, subject, and topic, in order to deepen the understanding of these centers and suggest future developmental directions. To this end, 202 research papers published between 2007 and 2025 were analyzed. The findings are as follows: First, research on Multicultural family support centers was conducted continuously until 2023, but decreased following the integration of Family Centers and Multicultural Family Support Centers. Second, the number of journal articles exceeded that of theses, likely due to limited accessibility to the centers. Third, quantitative and qualitative methods were employed almost equally, also reflecting these accessibility constraints. Fourth, the primary research subjects were center employees rather than program participants. Fifth, while the main research topic focused on center administration, a diverse range of sub-topics was also explored. Based on these results, this study suggests future directions for research and policy regarding Multicultural family support centers.

본 연구는 2007년부터 2025년까지 다문화가족지원센터 관련 논문 202편의 연구동 향을 분석하였다. 다문화가족지원센터 관련 논문을 연도별, 종류별, 유형별, 연구대상 별, 주제 영역별로 연구동향을 파악하기 위한 연구문제를 설정하고, 기초 통계치로 결 과를 제시하였다. 첫째, 2007년부터 2023년까지 꾸준히 논문이 발표되었으나, 다문화 가족지원센터라는 명칭을 사용하지 않고 가족센터로 명칭이 통합된 이후 2024년부터 는 다문화가족지원센터라는 제목이나 주제어로 논문이 발표되지 않았다. 둘째, 학술지 에 게재한 논문의 편수가 학위논문보다 2배 이상 많았으며, 학술지 논문 중 한국연구재 단 등재지에 발표된 논문이 대부분이었다. 셋째, 질적 연구나 문헌 연구에 비해 양적 연구가 많이 수행되기는 하였으나, 양적 연구와 문헌 연구는 급격하게 감소하는 추세 인 반면 질적 연구가 급격하게 증가하였다. 넷째, 연구대상은 인적 대상 중 다문화가족 지원센터 종사자가 절반 이상이었으며 이에 비해 센터 이용자/참여자와 다문화가족은 적은 수에 불과했다. 다섯째, 연구주제 영역은 다문화가족지원센터의 운영관리에 관한 연구가 가장 많았으며, 다음으로 인사관리에 관한 연구가 차지했다. 이에 비해 기본프 로그램이나 별도지원사업에 관한 연구는 그리 많지 않았다. 본 연구를 통해 다문화가 족지원센터에 대한 이해를 높이고 앞으로 어떠한 연구가 앞으로 필요한 지 추후 연구 방향을 제시하였다.

The purpose of this study was to analyze research trends regarding multicultural family support centers by year, research type, data collection method, subject, and topic, in order to deepen the understanding of these centers and suggest future developmental directions. To this end, 202 research papers published between 2007 and 2025 were analyzed. The findings are as follows: First, research on Multicultural family support centers was conducted continuously until 2023, but decreased following the integration of Family Centers and Multicultural Family Support Centers. Second, the number of journal articles exceeded that of theses, likely due to limited accessibility to the centers. Third, quantitative and qualitative methods were employed almost equally, also reflecting these accessibility constraints. Fourth, the primary research subjects were center employees rather than program participants. Fifth, while the main research topic focused on center administration, a diverse range of sub-topics was also explored. Based on these results, this study suggests future directions for research and policy regarding Multicultural family support centers.

The purpose of this study is to examine Korean cruise tourism research published in Journals listed in the Korea Citation Index(KCI) for the period of 1996~2018. Eighty nine full articles were selected for contents analysis. This study look at research topics and research design such as objectives related to subject area, sampling methods, data collection, statistical techniques and methodological aspects. Significant trends in research topics were identified and discussed to raise issues for future research. Cruise-related research were published in academic journals in the field of tourism, and the increase in related research has been noticeable since 2012. The themes of research are overwhelming related to marketing and tourism behavior, and the bias and unbalance of research is striking. Quantitative research was mainly conducted in the field of research methodology. Readers could gain insights into past research and ideas for future research from the study. The study concluded with limitations of this study and general suggestions for future cruise tourism research in Korea.

Interpreting research, like the profession itself, has been growing rapidly, both in sheer quantity and in diversity, with much of it being done by MA or PhD students who are also training to become practitioners. If in the past it was largely confined to conference interpreting in the simultaneous mode, interpreting research has now come to encompass the full range of modes, modalities and settings. While this trend is a welcome one, the implications for would-be researchers are daunting: how does one choose a topic? what paradigms and what neighboring disciplines does one turn to? how does one arrive at a realistic assessment of the time and resources required? how valid is an experimental study when it comes to such a real-life task? A list of twenty potential projects is presented along with a discussion of the methodology that may be best suited to each of them.

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.

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 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.

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.

This study presents an automated content analysis of 2,379 abstracts of research articles published in four major journals from 1997 to 2017. A total of 45 topics were extracted using the structural topic model (STM), a statistical text analytic method that allows us to infer latent topics from abstracts and to conduct statistical significance tests regarding the relationships between topic prevalence and abstract features. We found that while some topics (e.g., public health or online social movement) demonstrate increasing trends, others (e.g., third-person effect and communication strategies) reveal decreasing trends; authors in non-US-based institutions are more likely than those affiliated with US-based institutions to focus on the societal consequences of media (e.g., agenda-setting effect or public deliberation). Additionally, interesting relationships were found between topic prevalence and the number of article authors (e.g., popular topics among multi-author papers are lab-based experiments or large-scale campaign studies). Finally, topic-journal associations were identified.

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.

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.

무역학은 경제학, 경영학, 법학 등 여러 학문의 융합·통섭되어 다학제적 성격을 가지고 있기에, 연구주제의 동향을 파악하는 것은 연구자로서 매우 중요한 의미가 있다. 특히 2000년대 기술 의 발전으로 전자무역, 전자상거래에 대한 관심이 높아져 왔고, 특히 2016년 다보스포럼에서 ‘4차 산업혁명’이 중요 논의 과제로 부각된 이후 4차 산업혁명, 디지털 전환이라는 키워드가 경제·사회 전 영역에서 화두로 떠오름에 따라 실무뿐 아니라 학문으로서 ‘무역학’에도 상기 키워드를 기반으로 다양한 연구들이 이루어져 왔다. 하지만 무역학의 연구 분야가 다양한 주 제와 방법으로 확장되면서, 연구자들은 무역학 분야에서 어떤 연구들이 이루어져 왔는지 일목 요연하게 파악하기 어려워졌다. 2010년 이후 전자상거래 등 다양한 형태의 교역이 이미 이루어져 왔고, 한미 FTA 등 협정을 통해 관련 조항이 다루어진 바 있기에, 전자상거래, 전자무역 관련 연구들은 2000년대 이후 몇몇 연구자들에 의해 다루어져 왔다. 하지만 2010년대 중반 이후 4차 산업혁명이 글로벌 차원의 의제로 등장하면서 무역거래·결제, 물류산업에서 부터 데이터 통상 등 신통상의제에 이르기까 지 무역학 범분야적으로 4차 산업기술과 디지털 전환에 대한 연구가 왕성하게 이루어져 왔다. 본 연구는 ‘4차 산업혁명’과 ‘디지털 전환’이라는 주제가 무역·통상연구에 어떻게 구현되었는 지 연구동향을 파악하기 2004년부터 2021년 4월까지 한국연구재단의 학술지인용색인 DB를 통해 무역·통상분야 학술지 120여 개의 논문 초록·제목 등을 수집하였고, 이를 바탕으로 토픽 모델링 기법 중 LDA를 활용하여 연구동향 분석을 수행하였다. 분석결과 4차 산업혁명과 관련 하여 5개의 토픽(‘디지털 콘텐츠·개인정보보호’, ‘물류·디지털 전환’, ‘무역결제·핀테크’, ‘디지 털통상·분쟁’, ‘디지털통상·규범’)으로 구분되어 연구가 수행되었음을 알 수 있었다. 본 연구를 통해 디지털경제 시대의 무역·통상 등 신통상 정책의제 및 향후 연구방향에 대한 함의를 제시 할 수 있을 것으로 기대된다.

Purpose : As international trade studies has a multidisciplinary characte, it is very important as a researcher to figure out the trends of research topics. By investigating research trends in the field of international trade studies due to the 4th industrial revolution by using topic modeling, we will examine how research in international studies is being conducted. Research design, data, methodology : From 2004 to April 2021, abstracts and titles of 120 papers in international trade and commerce journals were collected through the Korea Citation Index DB of the National Research Foundation. Based on this, research trend analysis was conducted using LDA among topic modeling techniques carried out. Results : It can be seen that the research was conducted in five topics related to the 4th industrial revolution.(① digital contents·data privacy, ② Digital Transformation in logistics, ③ trade settlement·fintech, ④ digital Trade Dispute Settlement, ⑤ digital trade·regulation) Conclusions : This study is expected to provide implications for the new agenda of trade policy and future research directions such as international trade and commerce in the digital economy era.

This study maps domestic versus international trajectories in metaverse‑game research via text mining of 203 papers published in 2020–2024 (Korean text: 125; international: 78). We applied structural-equivalence (CONCOR) analysis and estimated topics via latent Dirichlet allocation (LDA). The top‑100 terms are visualized using word clouds. International keywords are led by systematic, technology, and rehabilitation/treatment, reflecting a strong emphasis on technological trends and clinical applications, whereas Korean studies foreground content and development alongside education and UX/immersion. Topic modeling yields four overseas themes (therapy/rehabilitation; mental health; digital accessibility; and education–entertainment convergence) and five Korean themes (technology & systems; UX/interface; healthcare & welfare; content; and platform). International work is dominated by literature studies (61/78; 78.2%), whereas Korean research centers on surveys and development (44 each; 35% apiece), with several case studies (20%). On the basis of these findings, we recommend more controlled experiments and meta‑analyses for Korean papers, and greater prototyping and field testing for international ones, in addition to stronger multidisciplinary collaboration.

In recent decades, the increasing frequency and severity of wildfires driven by climate change and other factors have led to a significant expansion of research on wildfire spread. This study presents a bibliometric analysis of wildfire spread research from 1982 to 2024. We compiled a dataset of scholarly publications and applied cluster analysis to identify major research topics. Using silhouette scores, we determined an optimal cluster number of approximately 20, and visualized the topic clusters with a two-dimensional embedding. We also analyzed publication trends over time, citation patterns, and keyword co-occurrence networks to understand the evolution and impact of each research topic. The results reveal a dramatic growth in wildfire spread studies over the past decade. We identified several distinct research clusters, including climate change impacts, fire behavior modeling, remote sensing applications, wildfire management strategies, and ecological effects of fires, among others. Some topics, such as climate-related wildfire research and remote sensing, have grown especially rapidly in recent years. Citation analysis indicates that certain longstanding research areas carry high influence, while emerging topics are gaining attention. The keyword network highlights interdisciplinary connections linking climate, ecology, and technology in wildfire research. This comprehensive overview of the wildfire spread research landscape offers insights into past developments and emerging directions, which can inform future research and policymaking in wildfire management.

This study systematically examines how research on AI-enabled human resource management (AI-enabled HRM) has evolved in terms of its thematic structure within organizational contexts. To this end, we applied BERTopic-based topic modeling to 797 articles on AI-enabled HRM retrieved from the Web of Science database. The analysis identified 20 topics, among which five dominant themes— adoption and effects, efficiency and strategy, managers and professionals, ethics and collaboration, and recruitment—each accounted for more than 5% of the total corpus and collectively defined the core structure of the AI-enabled HRM literature. A synthesis of the topic distributions and their temporal patterns indicates that AI-enabled HRM research has progressed from an initial focus on technological adoption and efficiency toward greater attention to organizational use, managerial interpretation, domain-specific applications, and governance-related concerns. In particular, the results suggest that the effects of AI-enabled HRM do not arise automatically from technological capabilities alone. Rather, they are contingent upon organizational preparedness, the interpretive and decision-making roles of managers and professionals, the characteristics of application domains, and management practices that support accountability and trust. This study provides a data-driven thematic map of AI-enabled HRM research that integrates previously fragmented research streams into a coherent field-level overview. The synthesis suggests that academic discussions of AI-enabled HRM are shaped not only by technological potential but also by organizational and governance conditions. Future research should move beyond general claims of effectiveness by identifying conditions and evaluating which governance arrangements support trustworthy AI-enabled HRM across application domains and stakeholder groups, including employees.

본 연구의 목적은 이민자의 디지털 불평등 관련 해외 연구의 주요 이슈와 연구 경향을 분석하는 것이다. 이를 위해 2000년부터 2023년 2월까지 Web of Science 학술지에 게재된 이민자의 디지털 불평등 관련 연구 총 401편의 논문을 대상으로 논문 건수 추이, 연구 분야, DLA 토픽모델링, 단어네트워크 분석을 통해 주요 연구 주제를 살펴보았다. 분석 결과, 시기별 이민자의 디지털 불평등 관련 논문 건수는 2006년 2편을 시작으로 이후 전반적으로 증가하는 추세이고, 특히 2020년 이후 관련 연구가 급증한 것으로 나타났다. 이민자의 디지털 불평등 연구를 주도하는 분야는 사회과학, 의약학, 융복합 등 다양한 학문 분야에서 이루어지고 있는 것으로 나타났다. 토픽모델링 및 단어네트워크 분석을 통해 ‘이민자의 디지털 보건 불평등’, ‘코로나19 팬데믹과 이민자의 디지털 불평등’, ‘이민자의 디지털 교육 불평등’, ‘디지털 시대와 이민자 불평등’, ‘이주노동자의 디지털 불평등’5개의 토픽으로 분류되었다. 마지막으로 ‘이민자 의 디지털 보건 불평등’과 ‘디지털 시대와 이민자 불평등’등 디지털 환경발전의 부작용 및 이민자의 생존과 직결된 분야와 관련된 주제에 대한 관심이 높아지는 것으로 나타났다.

The study aims to analyze key issues and research trends in international research on the digital inequality of immigrants. Based on a total of 401 articles on digital inequality of immigrants published in Web of Science registered journals between 2000 and February 2023, the authors identified key research topics. The analysis reveals an increasing number of articles on immigrants’ digital inequality, starting with two articles in 2006, and showing a sharp increase since 2020. Research areas on the digital inequality of immigrants are led by various academic fields, including social science, medicine, and multidisciplinary studies. Five topics were identified through topic modeling and word network analysis: ‘immigrants’ digital inequality in health’, ‘COVID-19 and immigrants’ digital inequality’, ‘Immigrants’ digital inequality in education’, ‘Immigrants’ inequality in the digital age’, and ‘Digital inequality of migrant workers’. It was shown that topics of ‘immigrants’ digital inequality in health’ and ‘Immigrants’ inequality in the digital age’ have been attracting significant attention.

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.

본 연구는 2001년부터 2020년까지 문헌정보학 SSCI 85종 학술지에 게재된 55,442편의 학술논문의 논문제목과 초록을 기반으로 다이나믹토픽모델링을 수행하여, 문헌정보학 분야의 연도별 흐름에 따른 연구 주제 추이를 분석하였다. 그 결과, 10개의 토픽에서 도서관경영(장서개발 및 관리, 도서관평가, 도서관 지식경영, 기획 및 활성화), 정보학(계량정보학, 정보이용행태․이용자연구, 의료정보, 정보시스템), 도서관 서비스(도서관교육․정보리터러시), 도서관체계(도서관 시책 및 정책)에 따른 4개의 대분류를 파악하였다. 연도별 흐름에 따라 정보학 영역의 경우, 계량정보학 연구 주제가 학술지단위에서 논문단위로 변화되고 있었으며, 최근 도서관경영 영역의 경우, 이용자의 의견과 감정에 관련한 연구가 최근 등장하였다. 도서관서비스 연구영역은 20년간 안정적인 연구 주제로 그 양상이 보다 심화되고 견고해졌음을 확인할 수 있었다. 그리고 최근에는 모바일과 소셜미디어와 관련한 연구가 진행 중인 것으로 나타났다. 한편, 정보학영역 하위주제로 의료정보와 관련한 연구가 비중 있게 등장해, 문헌정보학의 간학문적인 특징이 잘 나타난 결과라 판단하였다.

This study applied dynamic topic modeling on titles and abstracts of 55,442 academic papers in 85 SSCI journals from 2001 to 2020 in order to analyze research topic trend in library and information science. The analysis revealed four major themes of library management, informatics, library service, and library system in 10 major topics. The results also showed subtopics in information science and library management topic areas to change over time, while library service remained stable over 20 years to establish itself as a robust topic. In addition, medical information emerged as a significant sub-topic of informatics, thus exemplifying the interdisciplinary characteristics of library and information science field.