ArticleAdvances in medical education and practice2024
The State of Mastery Learning in Pediatric Graduate Medical Education: A Scoping Review.
Article in Advances in medical education and practice, 2024. 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.
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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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Who cites it
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Authors and funding
4 authors.
Funding
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Abstract
Objective: The aim of this study was to characterize the state of mastery learning interventions, identify gaps in current approaches, and highlight opportunities to improve the rigor of ML in pediatric graduate medical education (GME) training programs. Methods: In October 2022, we searched Ovid, PubMed, Scopus, and ERIC. Two reviewers independently screened 165 citations and reviewed the full manuscripts of 20 studies. We developed a modified data abstraction tool based on the Recommendations for Reporting Mastery Education Research in Medicine (ReMERM) guidelines and extracted variables related to mastery learning curricular implementation and design and learner assessment. Results: Eleven studies of ML approaches within pediatric GME were included in the full review, with over half published after 2020. ML interventions were used to teach both simple and complex tasks, often in heterogeneous learner groups. While deliberate practice and feedback were consistently reported features of ML in pediatrics, opportunities for improvement include clearly defining mastery, conducting learning over multiple sessions, presenting sufficient validity evidence for assessment tools, adhering to rigorous standard setting processes, and reporting psychometric data appropriate for ML. Conclusion: In pediatric GME, ML approaches are in their infancy. By addressing common shortcomings in the existing literature, future efforts can improve the rigor of ML in pediatric training programs and its impact on learners and patients.
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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.