Purpose
This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI).
Methods
This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivors", "adolescent", and "child" as the main search terms. After pre-processing, analyses were conducted on centrality (degree and eigenvector), cohesion (community), and topic modeling.
Results
For centrality, the top 10 keywords included "treatment", "factor", "intervention", "group", "radiotherapy", "health", "risk", "measurement", "outcome", and "quality of life". In terms of cohesion and topic analysis, three categories were identified as the major research trends: "treatment and complications", "adaptation and support needs", and "management and quality of life".
Conclusion
The keywords from the three main categories reflected interdisciplinary identification. Many studies on adaptation and support needs were identified in our analysis of nursing literature. Further research on managing and evaluating the quality of life among CACS must also be conducted.

Citations

• A Text Network Analysis of Nurse Managers’ Feedback Journals
  Naru Kang, Shinhye Ahn, Hye Won Jeong
  CIN: Computers, Informatics, Nursing.2026;[Epub]     CrossRef
• Evaluation of a Problem-Based Learning Program’s Effect on Artificial Intelligence Ethics Among Japanese Medical Students: Mixed Methods Study
  Yuma Ota, Yoshikazu Asada, Saori Kubo, Takeshi Kanno, Machiko Saeki Yagi, Yasushi Matsuyama
  JMIR Medical Education.2026; 12: e84535.     CrossRef
• Artificial intelligence in healthcare administration: Topic modeling with InfraNodus
  Joko Gunawan
  Journal of Healthcare Administration.2024; 3(1): 1.     CrossRef
• Value co-creation in shared mobility: The case of carpooling in China
  Chao Tian, Kai Tu, Haiqing Sui, Qi Sun
  Technological Forecasting and Social Change.2024; 205: 123481.     CrossRef
• ChatGPT integration within nursing education and its implications for nursing students: A systematic review and text network analysis
  Joko Gunawan, Yupin Aungsuroch, Jed Montayre
  Nurse Education Today.2024; 141: 106323.     CrossRef
• Climate-Smart Agriculture: A Path to Sustainable Food Production
  Nuzhat Khan, Mohamad Anuar Kamaruddin, Usman Ullah Sheikh, Muhammad Paend Bakht, Mohd Norzali Haji Mohd
  Journal of Natural Science Review .2024; 2(Special.Is): 130.     CrossRef
• Exploring Korean adolescent stress on social media: a semantic network analysis
  JongHwi Song, JunRyul Yang, SooYeun Yoo, KyungIn Cheon, SangKyun Yun, YunHee Shin
  PeerJ.2023; 11: e15076.     CrossRef
• Research trends over 10 years (2010-2021) in infant and toddler rearing behavior by family caregivers in South Korea: text network and topic modeling
  In-Hye Song, Kyung-Ah Kang
  Child Health Nursing Research.2023; 29(3): 182.     CrossRef
• Perspectives of Frontline Nurses Working in South Korea during the COVID-19 Pandemic: A Combined Method of Text Network Analysis and Summative Content Analysis
  SangA Lee, Tae Wha Lee, Seung Eun Lee
  Journal of Korean Academy of Nursing.2023; 53(6): 584.     CrossRef
• An Overview of Cognitive Reserve in Aging Based on Keyword Network Analysis
  Jihyun Kim, Mi So Kim
  INQUIRY: The Journal of Health Care Organization, .2022;[Epub]     CrossRef

Purpose
The purpose of this study was to identify human papillomavirus (HPV) vaccination research trends by visualizing a keyword network.
Methods
Articles about HPV vaccination were retrieved from the PubMed and Web of Science databases. A total of 1,448 articles published in 2006~2016 were selected. Keywords from the abstracts of these articles were extracted using the text mining program WordStat and standardized for analysis. Sixty-four keywords out of 287 were finally chosen after pruning. Social network analysis using NetMiner was applied to analyze the whole keyword network and the betweenness centrality of the network.
Results
According to the results of the social network analysis, the central keywords with high betweenness centrality included “health education”, “health personnel”, “parents”, “uptake”, “knowledge”, and “health promotion”.
Conclusion
To increase the uptake of HPV vaccination, health personnel should provide health education and vaccine promotion for parents and adolescents. Using social media, governmental organizations can offer accurate information that is easily accessible. School-based education will also be helpful.

Citations

• Application of Text Mining Techniques on Scholarly Research Articles: Methods and Tools
  Khusbu Thakur, Vinit Kumar
  New Review of Academic Librarianship.2022; 28(3): 279.     CrossRef