Literature review of complementary and alternative therapies: using text mining and analysis of trends in nursing research

Abstract Purpose This study aimed to review the literature on complementary and alternative therapies, utilizing text mining and trend analysis in nursing research. As CAM becomes increasingly prevalent in healthcare settings, a comprehensive understanding of the current research landscape is essential to guide evidence-based practice, inform clinical decision-making, and ultimately enhance patient outcomes. Method This study aimed to identify CAM-related literature published from 2018 to 2023. Using the search terms 'complementary therap*', 'complementary medicine', 'alternative therap*', and 'alternative medicine', we performed a comprehensive search in eight databases, including EMBASE, Cochrane Central, PubMed Central, Korea Education and Research Information Service (RISS), Web of Science, KMbase, KISS, and CINAHL. From the text network and topic modeling analysis of 66,490 documents, 15 topics were identified. These topics were classified into two nursing-related topics through an academic classification process involving three doctors with doctoral degrees, three nurses, and three pharmacists. Based on the classified topics, research trends were comparatively analyzed by re-searching the database for 12 nursing and 22 non-nursing literature. Result This study found that in nursing literature, yoga is used to improve mental symptoms such as stress and anxiety. In non-nursing literature, most of the experimental studies on complementary and alternative therapies were conducted in a randomized manner, confirming that a variety of physiological and objective indicators were used. Additionally, it was discovered that there were differences in the diversity of research subjects and research design methods for the same intervention method. Therefore, future research should focus on broadening the scope of subjects and measurement tools in nursing studies. Additionally, such studies should be conducted with randomization and generalizability in the experimental design in mind. Conclusion This study employed text network analysis and text mining to identify domestic and international CAM research trends. Our novel approach combined big data-derived keywords with a systematic classification method, proposing a new methodological strategy for trend analysis. Future nursing research should focus on broadening the scope of subjects, diversifying measurement tools, and emphasizing randomization and generalizability in experimental designs.