Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightFuture research is recommended to extend this approach by incorporating longitudinal data to examine how these structural profiles evolve over time.
📌 3 Maddede Bu Makale
Abstract
This study aims to examine global health inequalities from a multidimensional perspective through structural profiles. Going beyond traditional linear analyses, the effects of income inequality, education level and health expenditures on health outcomes are examined using cluster analysis. Sixty-six countries were evaluated within the scope of the research. Hierarchical clustering (Ward method) and K-means methods were used together to determine the structural groups. The validity of the cluster structure was tested using the Silhouette coefficient. Although different numbers of clusters were examined it was concluded that the three-cluster structure was the most suitable solution in terms of interpretability and theoretical consistency. The findings showed that countries were grouped under three different structural profiles. ANOVA and Welch tests revealed significant differences among clusters, particularly in basic health indicators such as child mortality, maternal mortality and life expectancy. In general, countries with lower income inequality and stronger health system capacity experienced better health outcomes. However, it is noteworthy that this relationship did not manifest in the same way across contexts. This study reveals that health inequalities can be explained more comprehensively not only by individual variables but also by structural determinants. The clustering approach offers a more holistic perspective than traditional analyses by making nonlinear, context-sensitive relationships visible. The findings highlight the importance of considering country-specific structural conditions in policy-making processes. Future research is recommended to extend this approach by incorporating longitudinal data to examine how these structural profiles evolve over time.