Randomized, controlled study evaluating multi-disciplinary team-based learning (MDTBL) as optimal teaching paradigm for residents, comparing with problem-based learning (PBL) and lecture-based learning (LBL)

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Purpose: This study aims to compare multi-disciplinary team-based learning (MDTBL), problem-based learning (PBL) and lecture-based learning (LBL) on the learning outcomes and experiences of medical students. Methods: A randomized controlled study was designed to recruit 30 medical students with a minimum of one year of clinical experience and 45 with less than one year of clinical experience to take a course on diagnostic imaging of pulmonary nodules from September 15, 2022, to December 31, 2023. The participants were randomly assigned to the MDTBL group, PBL group, and LBL group to complete a full-course curriculum. Before, during, and after the learning phases, all participants underwent identical theoretical assessments and received a score on their learning progress. In the MDTBL and PBL groups, group discussions were permitted. After the completion of the learning phase, self-evaluation and course satisfaction were assessed through a questionnaire. Results: The authors collected basic information on participants to ensure comparability between groups. All groups showed significant improvement in their diagnostic imaging of pulmonary nodules, with the MDTBL group demonstrating notably higher gains in theoretical knowledge and case analysis skills (P < 0.05). The learning participation scale indicated that student engagement in the MDTBL group was higher than in the other two groups (P < 0.05). Additionally, the MDTBL group perceived the course as more engaging and enjoyable. Conclusions: This study demonstrates that the MDTBL teaching model, as an innovative approach, excels in enhancing knowledge acquisition, collaborative skills, and clinical practice application skills in medical students. It positions itself as a valuable teaching model for future medical education, providing educators with a new toolkit for training specialists.

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