John T Paige, Deborah D Garbee, Qingzhao Yu, John Zahmjahn, Raquel Baroni de Carvalho, Lin Zhu, Vadym Rusnak, Vladimir J Kiselov
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引用次数: 0
Abstract
Background: The evidence for the conventional wisdom that debriefing quality determines the effectiveness of learning in simulation-based training is lacking. We investigated whether the quality of debriefing in using simulation-based training in team training correlated with the degree of learning of participants.
Methods: Forty-two teams of medical and undergraduate nursing students participated in simulation-based training sessions using a two-scenario format with after-action debriefing. Observers rated team performance with an 11-item Teamwork Assessment Scales (TAS) instrument (three subscales, team-based behaviours (5-items), shared mental model (3-items), adaptive communication and response (3-items)). Two independent, blinded raters evaluated video-recorded facilitator team prebriefs and debriefs using the Objective Structured Assessment of Debriefing (OSAD) 8-item tool. Descriptive statistics were calculated, t-test comparisons made and multiple linear regression and univariate analysis used to compare OSAD item scores and changes in TAS scores.
Results: Statistically significant improvements in all three TAS subscales occurred from scenario 1 to 2. Seven faculty teams taught learners with all scores ≥3.0 (except two) for prebriefs and all scores 3.5 (except one) for debriefs (OSAD rating 1=done poorly to 5=done well). Linear regression analysis revealed a single statistically significant correlation between debrief engagement and adaptive communication and response score without significance on univariate analysis.
Conclusions: Quality of debriefing does not seem to increase the degree of learning in interprofessional education using simulation-based training of prelicensure student teams. Such a finding may be due to the relatively high quality of the prebrief and debrief of the faculty teams involved in the training.
期刊介绍:
Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences.
In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification.
In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.