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.