Introduction: International interest in peer-teaching and peer-assisted learning (PAL) during undergraduate medical programs has grown in recent years, reflected both in literature and in practice. There, remains however, a distinct lack of objective clarity and consensus on the true effectiveness of peer-teaching and its short- and long-term impacts on learning outcomes and clinical practice.
Objective: To summarize and critically appraise evidence presented on peer-teaching effectiveness and its impact on objective learning outcomes of medical students.
Method: A literature search was conducted in four electronic databases. Titles and abstracts were screened and selection was based on strict eligibility criteria after examining full-texts. Two reviewers used a standard review and analysis framework to independently extract data from each study. Discrepancies in opinions were resolved by discussion in consultation with other reviewers. Adapted models of "Kirkpatrick's Levels of Learning" were used to grade the impact size of study outcomes.
Results: From 127 potential titles, 41 were obtained as full-texts, and 19 selected after close examination and group deliberation. Fifteen studies focused on student-learner outcomes and four on student-teacher learning outcomes. Ten studies utilized randomized allocation and the majority of study participants were self-selected volunteers. Written examinations and observed clinical evaluations were common study outcome assessments. Eleven studies provided student-teachers with formal teacher training. Overall, results suggest that peer-teaching, in highly selective contexts, achieves short-term learner outcomes that are comparable with those produced by faculty-based teaching. Furthermore, peer-teaching has beneficial effects on student-teacher learning outcomes.
Conclusions: Peer-teaching in undergraduate medical programs is comparable to conventional teaching when utilized in selected contexts. There is evidence to suggest that participating student-teachers benefit academically and professionally. Long-term effects of peer-teaching during medical school remain poorly understood and future research should aim to address this.
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http://dx.doi.org/10.2147/AMEP.S14383 | DOI Listing |
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School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK.
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December 2024
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
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December 2024
Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Jeollanam-do, Republic of Korea.
Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed at improving the interpretability and utility of nuclear medicine protocols. We discuss advanced image generation algorithms designed to recover details from low-dose scans, uncover information hidden by specific radiopharmaceutical properties, and enhance the sensing of physiological processes.
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