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Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling., Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling., Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women.", Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.

Immigrants in Korea face numerous difficulties in seeking medical services due to language and cultural differences. Providing medical services to them could be beyond the institutional capacity of the host country owing to factors such as, physical and psychological problems, social unrest, language barriers, and problems adapting to unfamiliar environments. According to Andersen's health service use behavioral model, we used a multifaceted approach to explore the factors influencing the unmet healthcare needs of immigrants in Korea from the Korean health system. This cross-sectional secondary analysis study used data from the 2019 Korea Community Health Survey of 3524 immigrants. Their unmet healthcare needs were calculated using a complex, weighted sample design. Group differences in categorical variables were analyzed using the Rao-Scott chi-square test. Logistic regression analysis was used to analyze the association between unmet healthcare needs and ageing factors. Overall, 262 (7.4%) of surveyed immigrants experienced unmet healthcare needs. Factors influencing unmet healthcare needs were being a woman (OR = 1.41, 95% CI = 1.03-1.94), national primary livelihood security receiver (OR = 1.44, 95% CI = 1.29-1.68), stress (OR = 1.34, 95% CI = 1.26-1.45), perceived health status (poor: OR = 2.35, 95% CI = 1.58-3.52), and perceived health status (moderate: OR = 1.62, 95% CI = 1.18-2.20). Policymakers could focus on these predictors when formulating policy strategies to reduce unmet health care needs. In addition, by effectively delivering services that meet the unmet healthcare needs of immigrants, their right to health is protected.