A scientometric analysis of the thematic structure and analysis of the convergence mapping of medical education.
Abstract
This study investigates the thematic structure and conceptual dynamics of research in medical education using scientometric and network analysis approaches. Despite the growing volume of literature in this field, few studies have comprehensively mapped its evolving patterns and interdisciplinary connections. To address this gap, a descriptive-analytical method was employed, focusing on two time periods: 2001–2010 and 2016–2025. Articles indexed in the Web of Science (WoS) database formed the statistical population, selected due to WoS’s comprehensive and interdisciplinary coverage. A keyword-based search strategy was developed through expert consultation and refined with artificial intelligence tools. Data from over 56,000 articles were collected, cleaned, and standardized using PreMap Ravar software. Core keywords were identified through Bradford’s law, and thematic structures were visualized using VOSviewer, Ucinet, and NetDraw software. The study analyzed word co-occurrence patterns, thematic clusters, and centrality indices to uncover key research areas and conceptual shifts over time. Findings reveal a growing interdisciplinary trend in medical education, with increasing integration of fields such as information technology, management, and artificial intelligence. Emerging topics include virtual education, simulation, evidence-based learning, and interprofessional collaboration. The analysis also highlights the expanding global interest in network-based evaluation of scientific fields.This research contributes to a deeper understanding of the intellectual landscape of medical education and provides valuable insights for researchers, policymakers, and educators. It underscores the importance of network analysis in mapping complex research domains and guiding strategic development in academic and institutional contexts.
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