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Expanding Interdisciplinarity: A bibliometric study of medical education using the MEJ-24

Maggio, L.; Costello, J. A.; Ninkov, A.; Frank, J.; Artino, A. R.

2023-03-24 scientific communication and education
10.1101/2023.03.22.533841 bioRxiv
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IntroductionInterdisciplinary research has been deemed to be critical in solving societys wicked problems, including those relevant to medical education. Medical education research has been assumed to be interdisciplinary. However, researchers have questioned this assumption. The present study, a conceptual replication, provides an analysis using a larger dataset and bibliometric methods to bring more clarity to our understanding on the nature of medical education interdisciplinarity or lack thereof. MethodThe authors retrieved the cited references of all published articles in 24 medical education journals between 2001-2020 from the Web of Science (WoS). We then identified the WoS classifications for the journals of each cited reference. ResultsThe 24 journals published 31,283 articles referencing 723,683 publications. We identified 493,973 (68.3%) of those cited references in 6,618 journals representing 242 categories, which represents 94% of all WoS categories. Close to half of all citations were categorized as "education, scientific disciplines" and "healthcare sciences and services". Over the two decades studied, we observed consistent growth in the number of references in other categories, such as education, educational research, and nursing. Additionally, the variety of categories represented has also increased from 182 to 233 to include a diversity of topics such as business, management, and linguistics. DiscussionThis study corroborates prior work while also expanding it. Medical education research is built upon a limited range of fields referenced. Yet, the growth in categories over time and the ongoing increased diversity of included categories suggests interdisciplinarity that until now has yet to be recognized and represents a changing story.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.