Objectives: To summarize current available data on simulation-based training in resuscitation for health care professionals.
Data Sources: MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC, Web of Science, Scopus and reference lists of published reviews.
Study Selection: Published studies of any language or date that enrolled health professions' learners to investigate the use of technology-enhanced simulation to teach resuscitation in comparison with no intervention or alternative training.
Data Extraction: Data were abstracted in duplicate. We identified themes examining different approaches to curriculum design. We pooled results using random effects meta-analysis.
Data Synthesis: 182 studies were identified involving 16,636 participants. Overall, simulation-based training of resuscitation skills, in comparison to no intervention, appears effective regardless of assessed outcome, level of learner, study design, or specific task trained. In comparison to no intervention, simulation training improved outcomes of knowledge (Hedges' g) 1.05 (95% confidence interval, 0.81-1.29), process skill 1.13 (0.99-1.27), product skill 1.92 (1.26-2.60), time skill 1.77 (1.13-2.42) and patient outcomes 0.26 (0.047-0.48). In comparison with non-simulation intervention, learner satisfaction 0.79 (0.27-1.31) and process skill 0.35 (0.12-0.59) outcomes favored simulation. Studies investigating how to optimize simulation training found higher process skill outcomes in courses employing "booster" practice 0.13 (0.03-0.22), team/group dynamics 0.51 (0.06-0.97), distraction 1.76 (1.02-2.50) and integrated feedback 0.49 (0.17-0.80) compared to courses without these features. Most analyses reflected high between-study inconsistency (I(2) values >50%).
Conclusions: Simulation-based training for resuscitation is highly effective. Design features of "booster" practice, team/group dynamics, distraction and integrated feedback improve effectiveness.
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http://dx.doi.org/10.1016/j.resuscitation.2013.04.016 | DOI Listing |
Sci Rep
December 2024
School of Computer and Information Engineering, Hubei Normal University, Huangshi, 435002, China.
For finely representation of complex reservoir units, higher computing overburden and lower spatial resolution are limited to traditional stochastic simulation. Therefore, based on Generative Adversarial Networks (GANs), spatial distribution patterns of regional variables can be reproduced through high-order statistical fitting. However, parameters of GANs cannot be optimized under insufficient training samples.
View Article and Find Full Text PDFBMC Med Educ
December 2024
Department of Social and Preventive Medicine, Center for Public Health, Medical University of Vienna, Vienna, Austria.
Background: Cancer remains a critical global health issue requiring a comprehensive interdisciplinary approach for effective treatment. Interprofessional education (IPE) is essential for overcoming barriers to collaboration among healthcare professionals and fostering efficient teamwork in cancer care.
Objective: This systematic scoping review aims to explore the role of IPE in enhancing interprofessional collaboration within cancer care by mapping and synthesizing the implementation, impact, and evaluation strategies of patient-centered IPE programs in this field.
Adv Simul (Lond)
December 2024
University of Ottawa Skills & Simulation Centre, The Ottawa Hospital, Civic Campus, Loeb Research Building, 1st floor, 725 Parkdale Ave., Ottawa, ON, K1Y 4E9, Canada.
Simulation-based education often involves learners or teams attempting to manage situations at the limits of their abilities. As a result, it can elicit emotional reactions in participants. These emotions are not good or bad, they simply are.
View Article and Find Full Text PDFCureus
November 2024
Department of Undergraduate Education, Royal Blackburn Hospital, Blackburn, GBR.
Introduction Transitioning from a medical student to a foundation doctor presents numerous challenges, particularly in managing on-call duties that require quick decision-making, clinical skills, and prioritisation under pressure. The Simulation On-Call (SOC) program was developed as a one-day, immersive simulation event to equip final-year medical students with the skills and confidence needed for these responsibilities. Methods The SOC program is an annual event held for final-year medical students at the Royal Blackburn Hospital, Blackburn, UK.
View Article and Find Full Text PDFInt J Antimicrob Agents
December 2024
Department of Pharmacy, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510163, China. Electronic address:
Despite the widespread use of voriconazole in antifungal treatment, its high pharmacokinetic and pharmacodynamic variability may lead to suboptimal efficacy, especially in intensive care unit (ICU) patients. Machine learning (ML), an artificial intelligence modeling approach, is increasingly being applied to personalized medicine. The effectiveness of ML models for predicting voriconazole blood concentrations in ICU patients, compared to traditional population pharmacokinetics (popPK) models, has been uncertain until now.
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