RESUMEN
Purpose@#The aim of this study was to identify core keywords and topic groups in the “adolescent pregnancy” field of research for a better understanding of research trends in the past 10 years. @*Methods@#Topics related to adolescent pregnancy were extracted from 3,819 articles that were published in journals between January 2013 and July 2023. Abstracts were retrieved from five databases (MEDLINE, CINAHL, Embase, RISS, and KISS). Keywords were extracted from the abstracts and cleaned using semantic morphemes. Text network analysis and topic modeling were performed using NetMiner 4.3.3. @*Results@#The most important keywords were “health,” “woman,” “risk,” “group,” “girl,” “school,” “service,” “family,” “program,” and “contraception.” Five topic groups were identified through topic modeling. Through the topic modeling analysis, five themes were derived: “health service,” “community program for school girls,” “risks for adult women,” “relationship risks,” and “sexual contraceptive knowledge.” @*Conclusion@#This study utilized text network analysis and topic modeling to analyze keywords from abstracts of research conducted over the past decade on adolescent pregnancy. Given that adolescent pregnancy leads to physical, mental, social, and economic issues, it is imperative to provide integrated intervention programs, including prenatal/postnatal care, psychological services, proper contraception methods, and sex education, through school and community partnerships, as well as related research studies. Nurses can play a vital role by actively engaging in prevention efforts and directly supporting and educating socially disadvantaged adolescent mothers, which could significantly contribute to improving their quality of life.
RESUMEN
Purpose@#This study aimed to develop a case-based nursing education program using generative artificial intelligence and to assess its usability and applicability in nursing curriculums. @*Methods@#The program was developed by following the five steps of the ADDIE model: analysis, design, development, implementation, and evaluation. A panel of five nursing professors served as experts to implement and evaluate the program. @*Results@#Utilizing ChatGPT, six program modules were designed and developed based on experiential learning theory. The experts’ evaluations confirmed that the program was suitable for case-based learning, highly usable, and applicable to nursing education. @*Conclusion@#Generative artificial intelligence was identified as a valuable tool for enhancing the effectiveness of case-based learning. This study provides insights and future directions for integrating generative artificial intelligence into nursing education. Further research should be attempted to implement and evaluate this program with nursing students.