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Updated: Sep 24, 2025

Simulator Training for Endovascular Neurosurgery
Published on: May 6, 2020
Impact of providing case-specific knowledge in simulation: a theory based study of learning
Jocelyn Cox1, Marion McGregor2, Dominic Giuliano3
1Department of Chiropractic, D'Youville College, Buffalo, New York, USA.
Background:
Simulation-based education (SBE) has been lauded for its ability to help students recognise and react appropriately to common and rare circumstances. While healthcare professions have started to implement SBE into their curriculum, there is no evidence to suggest which educational theory is best for implementation. This study explores the usage of cognitive load theory (CLT) and the unified theory of emotional learning (UTEL).
Study Design:
A mixed methods ordered-allocation cohort study.
Methods:
23 patient management teams were allocated into 2 groups. The first group received prior information about the simulation scenario; the second group did not. Each team had 1 student assigned to the role of doctor. The scenarios were filmed at time 1 (T1), time 2 (T2) and follow-up (F/U). The 'doctor' role was then graded with a validated checklist by a three-judge panel. The scores were evaluated to determine if prior information enabled better performance. Secondary analysis evaluated the role of gender on performance and also evaluated anxiety at the onset of the simulation.
Results:
23 doctors were evaluated. There was no difference between groups in performance (t=1.54, p=0.13). Secondary analysis indicated that gender did not play a role. There was no difference in anxiety between groups at baseline (t=0.67, p=0.51).
Conclusions:
Trends were observed, suggesting that when students enter a simulation environment with prior knowledge of the event they will encounter, their performance may be higher. No differences were observed in performance at T2 or F/U. Withholding information appeared to be an inappropriate proxy for emotional learning as no difference in anxiety was observed between groups at baseline. All trends require confirmation with a larger sample size.
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