Dimitrios Stefanidis, David Cook, Seyed-Mohammad Kalantar-Motamedi, Sharon Muret-Wagstaff, Aaron W Calhoun, Kasper G Lauridsen, John T Paige, Andrew Lockey, Aaron Donoghue, Andrew K Hall, Catherine Patocka, Janice Palaganas, Isabel T Gross, David Kessler, Julia Vermylen, Yiqun Lin, Michelle Aebersold, Todd P Chang, Jonathan Duff, Michaela Kolbe, Tonya Rutherford-Hemming, Sharon Decker, Amelia Collings, Mohammed Toseef Ansari
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引用次数: 0
Abstract
Background: Simulation has become a staple in the training of healthcare professionals with accumulating evidence on its effectiveness. However, guidelines for optimal methods of simulation training do not currently exist.
Methods: Systematic reviews of the literature on 16 identified key questions were conducted and expert panel consensus recommendations determined using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology.
Objective: These evidence-based guidelines from the Society for Simulation in Healthcare intend to support healthcare professionals in decisions on the most effective methods for simulation training in healthcare.
Results: Twenty recommendations on 16 questions were determined using GRADE. Four expert recommendations were also provided.
Conclusions: The first evidence-based guidelines for simulation training are provided to guide instructors and learners on the most effective use of simulation in healthcare.
期刊介绍：
Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare is a multidisciplinary publication encompassing all areas of applications and research in healthcare simulation technology. The journal is relevant to a broad range of clinical and biomedical specialties, and publishes original basic, clinical, and translational research on these topics and more: Safety and quality-oriented training programs; Development of educational and competency assessment standards; Reports of experience in the use of simulation technology; Virtual reality; Epidemiologic modeling; Molecular, pharmacologic, and disease modeling.
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