Daly Guris Rodrigo J, Miller Christina R, Schiavi Adam, Toy Serkan
Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
BMJ Simul Technol Enhanc Learn. 2021 Jun 16;7(6):548-554. doi: 10.1136/bmjstel-2020-000812. eCollection 2021.
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).
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 χ analyses to examine whether reported task load differences or demographic variables were associated with cluster membership.
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.
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.
了解学习者之间的表现差异可为优化医学教育提供有用的背景信息。这项试点研究旨在通过对两项随机对照模拟研究进行回顾性二次分析，探索一种将表现差异情境化的技术。一项研究聚焦于大声表达（非技术技能）；另一项研究聚焦于氧饱和度降低管理（技术技能）。
我们回顾性分析了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究每项研究中的参与者是否形成了同质的表现集群。然后，我们使用混合设计方差分析和χ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集群成员身份相关。
在这两个案例中，都出现了两个集群的解决方案；一个集群代表的学员相对于第二个集群中的同龄人表现更高。在每项原始研究中，集群成员身份与实验分配无关。集群成员之间没有明显的人口统计学差异。对于非技术技能，集群之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很快消失。大声表达方面表现出色的学员最初报告的任务负荷低于表现一般的学员，这种差异随着时间的推移而消失。在氧饱和度降低管理期间，表现与任务负荷之间没有关联。
这项试点研究表明，聚类分析可用于在模拟临床环境中客观识别技术和非技术技能方面表现出色的学员。非技术技能可能比纯技术技能更难教授和保持，并且任务负荷与初始非技术表现之间可能存在关联。需要进一步研究以了解哪些因素可能赋予内在的表现优势，这些优势是否能转化为临床实践表现，以及课程如何能最好地设计以推动针对个别学员的有针对性的改进。
