Debriefing Is Germane to Simulation-Based Learning: Parsing Cognitive Load Components and the Effect of Debriefing.

IF 2.1 3区 医学 Q3 HEALTH CARE SCIENCES & SERVICES Simulation in Healthcare-Journal of the Society for Simulation in Healthcare Pub Date : 2025-12-01 Epub Date: 2025-03-26 DOI:10.1097/SIH.0000000000000854
Christina R Miller, Sara K Greer, Serkan Toy, Adam Schiavi
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Abstract

Introduction: Cognitive load (CL) theory provides a framework for optimizing learning in simulation. Measures of CL components (intrinsic [IL], extraneous [EL] and germane [GL]) may inform simulation design but lack validity evidence. The optimal timing for CL assessment and contributions of debriefing to CL are not established.

Methods: This prospective observational study assessed self-reported CL for first-year anesthesiology residents during 10 individual-learner simulations. Following each simulation and before debriefing, participants completed 4 CL measures: Paas scale, National Aeronautics and Space Administration-Task Load Index (NASA-TLX), Cognitive Load Component questionnaire (CLC) and Cognitive Load Assessment Scales in Simulation (CLAS-Sim). After debriefing, participants repeated the Paas and CLAS-Sim.

Results: Twenty-nine first-year anesthesiology residents participated. Correlations were significant among all total CL measures ( r range = 0.51-0.69) and between CLC and CLAS-Sim IL (r = 0.66), EL (r = 0.41), and GL (r = 0.61) (all P < 0.01). We observed a significant interaction between total CL measures and case complexity, and a significant main effect of case complexity for CLC and CLAS-Sim IL, with no main effect for IL measure. The CLAS-Sim EL was higher ( P = 0.001) than respective CLC scales across cases, with no difference for GL. Participants reported higher CLAS-Sim GL after (versus before) debriefing ( P < 0.001), with no difference in IL, EL, or Paas scores.

Conclusions: This study provides further validity evidence for the CLAS-Sim and demonstrates generalizability in a different population of medical trainees. The CLAS-Sim GL increases following debriefing, reflecting expected learning, demonstrating initial GL scale validity evidence.

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述职对模拟学习的影响:分析认知负荷成分及述职的效果。
认知负荷(CL)理论为优化模拟学习提供了一个框架。CL成分(内在[IL]、外在[EL]和相关[GL])的测量可以为仿真设计提供信息,但缺乏有效性证据。目前还没有确定进行CL评估的最佳时机以及汇报对CL的贡献。方法:本前瞻性观察研究在10个个体学习者模拟中评估了麻醉住院医师第一年自我报告的CL。在每次模拟之后和汇报前,参与者完成4项CL测量:Paas量表、nasa任务负荷指数(NASA-TLX)、认知负荷成分问卷(CLC)和模拟认知负荷评估量表(CLAS-Sim)。汇报后,参与者重复Paas和CLAS-Sim。结果:29名一年级麻醉科住院医师参与。CLC与CLAS-Sim IL (r = 0.66)、EL (r = 0.41)、GL (r = 0.61)之间的相关性均有统计学意义(P < 0.01)。我们观察到总CL测量与病例复杂性之间存在显著的交互作用,并且病例复杂性对CLC和CLAS-Sim IL有显著的主效应,而IL测量没有主效应。CLAS-Sim EL在所有病例中高于各自的CLC量表(P = 0.001),而GL没有差异。参与者在汇报后(与之前相比)报告了更高的CLAS-Sim GL (P < 0.001), IL, EL或Paas评分没有差异。结论:本研究为CLAS-Sim提供了进一步的有效性证据,并在不同的医学培训生群体中展示了普遍性。汇报后,CLAS-Sim GL增加,反映了预期的学习,证明了最初的GL量表效度证据。
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来源期刊
CiteScore
4.00
自引率
8.30%
发文量
158
审稿时长
6-12 weeks
期刊介绍: Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare is a multidisciplinary publication encompassing all areas of applications and research in healthcare simulation technology. The journal is relevant to a broad range of clinical and biomedical specialties, and publishes original basic, clinical, and translational research on these topics and more: Safety and quality-oriented training programs; Development of educational and competency assessment standards; Reports of experience in the use of simulation technology; Virtual reality; Epidemiologic modeling; Molecular, pharmacologic, and disease modeling.
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