ArticleJournal of medical education and curricular development
Beyond the Binary: A Five-Pillar Framework for Hybrid Medical Education Post-Pandemic.
Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
Funding
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Abstract
The COVID-19 pandemic rapidly transformed medical education, exposing both challenges and opportunities in online, in-person, and hybrid instructional models. This Viewpoint proposes a five-pillar framework for effective hybrid medical education: curricular alignment, faculty development, interactive online pedagogy, flexibility with accountability, and data-driven feedback. Drawing on the experience of two medical students and the perspective of an anatomy professor, an award-winning educator and curriculum leader, we offer a framework for hybrid medical education. While our viewpoint centers on undergraduate medical education, we recognize concerns from graduate medical educators about downstream resident preparedness and the need for broader collaboration. Our framework addresses learner engagement, clinical preparedness, and equity. We recommend that academic leaders adopt and periodically audit curricula using this framework to ensure educational quality and readiness for future disruptions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.