Brick in the wall? Linking quality of debriefing to participant learning in team training of interprofessional students.

IF 1.6 2区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Statistics and Computing Pub Date : 2021-01-27 eCollection Date: 2021-01-01 DOI:10.1136/bmjstel-2020-000685
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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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.

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墙上的砖块?将跨专业学生团队培训中的汇报质量与学员学习联系起来。
背景:传统观点认为,汇报质量决定了模拟培训的学习效果,但这一观点缺乏证据。我们研究了在团队培训中使用模拟训练的汇报质量是否与参与者的学习程度相关:方法:42 支由医科和护理本科生组成的团队参加了模拟培训课程,课程采用两种情景模式,并配有行动后汇报。观察者使用 11 个项目的团队合作评估量表(TAS)工具(三个子量表:团队行为(5 个项目)、共享心理模型(3 个项目)、适应性交流和反应(3 个项目))对团队表现进行评分。两名独立的盲法评定员使用客观结构化汇报评估(OSAD)8 项工具对视频录制的主持人团队预汇报和汇报进行了评估。计算描述性统计数字,进行 t 检验比较,并使用多元线性回归和单变量分析来比较 OSAD 项目得分和 TAS 分数的变化:7 个教师团队为学员授课时,课前汇报得分全部≥3.0(2 人除外),课后汇报得分全部≥3.5(1 人除外)(OSAD 评分 1=做得差,5=做得好)。线性回归分析显示,汇报参与度与适应性交流和反应得分之间存在单项统计意义上的显著相关性,但在单变量分析中并不显著:结论:在对执业前学生团队进行模拟训练的跨专业教育中,汇报的质量似乎并不能提高学习程度。这一发现可能是由于参与培训的教师团队的预汇报和汇报质量相对较高。
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来源期刊
Statistics and Computing
Statistics and Computing 数学-计算机:理论方法
CiteScore
3.20
自引率
4.50%
发文量
93
审稿时长
6-12 weeks
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.
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