Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
Ho Ping Kong Centre for Excellence in Education and Practice, University Health Network, Toronto, Ontario, Canada.
Perspect Med Educ. 2018 Feb;7(1):23-32. doi: 10.1007/s40037-017-0392-7.
The ability to maintain good performance with low cognitive load is an important marker of expertise. Incorporating cognitive load measurements in the context of simulation training may help to inform judgements of competence. This exploratory study investigated relationships between demographic markers of expertise, cognitive load measures, and simulator performance in the context of point-of-care ultrasonography.
Twenty-nine medical trainees and clinicians at the University of Toronto with a range of clinical ultrasound experience were recruited. Participants answered a demographic questionnaire then used an ultrasound simulator to perform targeted scanning tasks based on clinical vignettes. Participants were scored on their ability to both acquire and interpret ultrasound images. Cognitive load measures included participant self-report, eye-based physiological indices, and behavioural measures. Data were analyzed using a multilevel linear modelling approach, wherein observations were clustered by participants.
Experienced participants outperformed novice participants on ultrasound image acquisition. Ultrasound image interpretation was comparable between the two groups. Ultrasound image acquisition performance was predicted by level of training, prior ultrasound training, and cognitive load. There was significant convergence between cognitive load measurement techniques. A marginal model of ultrasound image acquisition performance including prior ultrasound training and cognitive load as fixed effects provided the best overall fit for the observed data.
In this proof-of-principle study, the combination of demographic and cognitive load measures provided more sensitive metrics to predict ultrasound simulator performance. Performance assessments which include cognitive load can help differentiate between levels of expertise in simulation environments, and may serve as better predictors of skill transfer to clinical practice.
能够在低认知负荷下保持良好表现是专业能力的重要标志。在模拟训练的背景下纳入认知负荷测量可能有助于告知能力判断。本探索性研究调查了在床边超声检查背景下,专业知识的人口统计学标志物、认知负荷测量和模拟器性能之间的关系。
在多伦多大学,招募了 29 名具有不同临床超声经验的医学学员和临床医生。参与者回答了一份人口统计学问卷,然后使用超声模拟器根据临床病例进行有针对性的扫描任务。参与者在获取和解释超声图像的能力上进行评分。认知负荷测量包括参与者自我报告、基于眼睛的生理指标和行为测量。使用多层线性建模方法分析数据,其中观察结果按参与者进行聚类。
有经验的参与者在超声图像获取方面的表现优于新手参与者。两组在超声图像解释方面表现相当。超声图像获取性能由培训水平、先前的超声培训和认知负荷预测。认知负荷测量技术之间存在显著的收敛性。包括先前的超声培训和认知负荷作为固定效应的超声图像获取性能的边际模型为观察到的数据提供了最佳的整体拟合。
在这项原理验证研究中,人口统计学和认知负荷测量的组合提供了更敏感的指标来预测超声模拟器性能。包括认知负荷的绩效评估可以帮助区分模拟环境中的专业水平,并且可能成为技能转移到临床实践的更好预测指标。