Discovering Factors Causing Divorce Through Bibliometric Analysis - Sri Wahyuni; Satriya Pranata; Mohammad Fatkhul Mubin; Ernawati Ernawati (Iran University of Medical Sciences, 2024)
Background: Divorce is a global issue, and its rate continues to increase every year. There has been a great deal of research on divorce; however, there has not been any attempt to provide a thorough scientific mapping of the topic. The networks, patterns, and divorce trends can be revealed through bibliometric analysis. This study aimed to identify the network visualization, overlay visualization, and density visualization of the factors that cause divorce.  Methods: This research was a bibliometric analysis study. The keywords were identified by searching the database at dimensions application and followed the stages of the PRISMA (preferred reporting items for systematic reviews and meta-analyses). The type of publication chosen was articles limited to publication from 2019 to 2023, focusing on human society, health science, psychology, and biomedical fields. Data were analyzed using VOSViewer software, version 1.6.18, and then reviewed by the co-occurrence and co-authors.  Results: The underlying factors of divorce found in this bibliometric study are very diverse. Based on network visualization, there were 178 items in 7 clusters with 5123 links and a total link strength of 40770. Through overlay visualization, the research trend regarding the factors related to divorce was early marriage, attitude toward marriage, HIV testing, depressive symptoms, sexual abuse, the COVID-19 pandemic, and culture. Meanwhile, according to density visualization, the topics with low category visualization were marital burnout, sexual violence against women, non-suicidal self-injury, physical abuse, and sexual satisfaction.  Conclusion: Previous studies have found a lot about the factors causing divorce through qualitative and quantitative study methods, but still, few studies have focused on how to prevent divorce. For other researchers, choosing a topic that belongs to the low visualization category is recommended to explore themes that have not yet been widely researched.