检查新手麻醉实习模拟性能:两个集群的故事

IF 1.1 Q2 Social Sciences
R. D. Daly Guris, Christina R Miller, A. Schiavi, S. Toy
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引用次数: 1

摘要

了解学习者之间的表现差异可以为优化医学教育提供有用的背景。本初步研究旨在通过对两项随机对照模拟研究的回顾性二次分析,探索一种将表现差异置于背景下的技术。一项研究侧重于大声说话(非技术技能);另一个侧重于氧饱和度管理(技术技能)。方法回顾性分析2017年和2018年进行的两项独立模拟研究的数据。我们使用多元层次聚类分析来探讨每个研究的参与者是否形成同质的绩效聚类。然后,我们使用混合设计方差分析和χ2分析来检验报告的任务负荷差异或人口统计学变量是否与聚类隶属度相关。结果在这两种情况下,出现了双集群解决方案;一组代表受训人员相对于第二组的同行表现更高。在每个原始研究中,聚类隶属度独立于实验分配。集群成员之间没有明显的人口统计学差异。在非技术技能方面,组间的表现差异持续了至少8个月,但在技术技能的模拟训练后,这种差异很快消失了。在发言方面表现出色的人最初报告的任务负荷比表现一般的人低,这种差异随着时间的推移而消失。在去饱和管理期间,表现和任务负荷之间没有关联。本初步研究表明,聚类分析可用于客观地识别技术和非技术技能的高绩效受训人员,如在模拟临床环境中观察到的。非技术技能可能比纯技术技能更难教授和保留,并且在任务负荷和初始非技术性能之间可能存在关联。需要进一步的研究来了解哪些因素可能赋予固有的性能优势,这些优势是否转化为临床表现,以及如何最好地设计课程来推动个别受训者的有针对性的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Examining novice anaesthesia trainee simulation performance: a tale of two clusters
Introduction Understanding performance differences between learners may provide useful context for optimising medical education. This pilot study aimed to explore a technique to contextualise performance differences through retrospective secondary analyses of two randomised controlled simulation studies. One study focused on speaking up (non-technical skill); the other focused on oxygen desaturation management (technical skill). Methods We retrospectively analysed data from two independent simulation studies conducted in 2017 and 2018. We used multivariate hierarchical cluster analysis to explore whether participants in each study formed homogenous performance clusters. We then used mixed-design analyses of variance and χ2 analyses to examine whether reported task load differences or demographic variables were associated with cluster membership. Results In both instances, a two-cluster solution emerged; one cluster represented trainees exhibiting higher performance relative to peers in the second cluster. Cluster membership was independent of experimental allocation in each of the original studies. There were no discernible demographic differences between cluster members. Performance differences between clusters persisted for at least 8 months for the non-technical skill but quickly disappeared following simulation training for the technical skill. High performers in speaking up initially reported lower task load than standard performers, a difference that disappeared over time. There was no association between performance and task load during desaturation management. Conclusion This pilot study suggests that cluster analysis can be used to objectively identify high-performing trainees for both a technical and a non-technical skill as observed in a simulated clinical setting. Non-technical skills may be more difficult to teach and retain than purely technical ones, and there may be an association between task load and initial non-technical performance. Further study is needed to understand what factors may confer inherent performance advantages, whether these advantages translate to clinical performance and how curricula can best be designed to drive targeted improvement for individual trainees.
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来源期刊
BMJ Simulation & Technology Enhanced Learning
BMJ Simulation & Technology Enhanced Learning HEALTH CARE SCIENCES & SERVICES-
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