Applied Training in Conducting Scientometric Professional Studies and Mapping and Analyzing Medical Scientific Networks.

The document is a structured, applied training guide designed to bridge the educational gap in conducting professional scientometric studies, with a specific focus on the mapping and analysis of medical scientific networks. Scientometrics is presented as a key approach for systematically assessing k...

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Bibliographic Details
Published in:Revista Española de Educación Médica
Main Author: Mohammad Bagher Negahban
Format: Article
Language:English
Published: Universidad de Murcia 2025-10-01
Subjects:
Online Access:https://revistas.um.es/edumed/article/view/684541
Description
Summary:The document is a structured, applied training guide designed to bridge the educational gap in conducting professional scientometric studies, with a specific focus on the mapping and analysis of medical scientific networks. Scientometrics is presented as a key approach for systematically assessing knowledge structure, trends, and collaboration, which is indispensable in the rapidly evolving fields of medicine. The guide proposes a practical eight-step framework for the researcher, beginning with a clear definition of a research objective and thematic scope. Crucial steps include selecting authoritative data sources such as Web of Science or Scopus and formulating a precise search strategy using Boolean operators. Following data collection and refinement, the process focuses on network visualization and analysis. For analysis, it is recommended to export complete records to specialized tools such as VOSviewer, CiteSpace, and Gephi. These software programs are used to generate maps of co-authorship, co-citation, and co-word networks, which reveal the social, intellectual, and conceptual structure of a field. The document emphasizes that the interpretation is vital. This phase transforms visualizations and metrics into meaningful insights, which connect with research objectives and allow for the detection of emerging trends or knowledge gaps. Furthermore, the guide introduces advanced bias and integrity detection, using AI to identify anomalies such as citation circles or redundant patterns. Finally, the author underscores the need for clear, transparent, and reproducible reporting, ensuring that the findings serve as a strategic and reliable tool for decision-making and scientific policy.
ISSN:2660-8529