Scaffolding clinical reasoning of medical students with virtual patients: effects on diagnostic accuracy, efficiency, and errors
Scaffolding clinical reasoning of medical students with virtual patients: effects on diagnostic accuracy, efficiency, and errors
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DOI：
10.1515/dx-2018-0090
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发表时间：
2019-06-01
期刊：
影响因子：
3.5
通讯作者：
Schmidmaier, Ralf
中科院分区：
文献类型：
--
作者：
Braun, Leah T.;Borrmann, Katharina F.;Schmidmaier, Ralf
背景：理解临床推理是医学教育研究中的一大挑战。关于脚手架和反馈对医学生临床推理的影响，我们知之甚少。本研究的目的是测量问题表征(临床病例的认知表征)和有无反馈的结构化脚手架对医学生诊断效率和诊断错误特征的影响。方法：将148名高级医学生随机分为5组(2×2设计，对照组)。他们在电子学习环境中研究了15个虚拟临床案例(5个学习案例、5个初始评估案例和5个延迟评估案例)。在每个病例之后，他们陈述了他们的推定诊断并解释了他们的诊断结论。结果：两个不同评估阶段的诊断准确率(正确解决例数)和诊断效率(解决例数/总时间)均无显著差异[均值=2.2~3.3(标准差[SD]=0.79~1.31)，p=0.08/0.27和均值=0.070.12(SD=0.04~0.08)，p=0.16/0.32]。导致诊断错误的最主要原因是缺乏诊断技能(20%)、缺乏知识(18%)和过早关闭(17%)。结论：与对照组相比，结构化反射和表征支架都不能提高医学生的诊断准确率或效率。
Background: Understanding clinical reasoning is a major challenge in medical education research. Little is known about the influence of scaffolding and feedback on the clinical reasoning of medical students. The aim of this study was to measure the effects of problem representation (cognitive representation of a clinical case) and structured scaffolding for reflection with or without feedback on the diagnostic efficiency and characterization of diagnostic errors of medical students.Methods: One hundred and forty-eight advanced medical students were randomly assigned to one of five groups (2 x 2 design with a control group). They worked on 15 virtual clinical cases (five learning cases, five initial assessment cases, and five delayed assessment cases) in an electronic learning environment. After each case, they stated their presumed diagnosis and explained their diagnostic conclusion. Diagnostic accuracy, efficiency, and error distribution were analyzed.Results: The diagnostic accuracy (number of correctly solved cases) and efficiency (solved cases/total time) did not differ significantly between any of the groups in the two different assessment phases [mean = 2.2-3.3 (standard deviation [SD] = 0.79-1.31), p = 0.08/0.27 and mean = 0.070.12 (SD = 0.04-0.08), p = 0.16/0.32, respectively]. The most important causes for diagnostic errors were a lack of diagnostic skills (20%), a lack of knowledge (18%), and premature closure (17%).Conclusions: Neither structured reflections nor representation scaffolding improved diagnostic accuracy or efficiency of medical students compared to a control group when working with virtual patients.