scholarly journals 1062: FORMAL SIMULATION EDUCATION USE IN THE PEDIATRIC CARDIAC ICU

2021 ◽  
Vol 50 (1) ◽  
pp. 529-529
Author(s):  
Kathy Mendieta ◽  
Kelly Craighead ◽  
Isaura Diaz
Keyword(s):  
Author(s):  
Richard McNutt ◽  
Matthew Tews ◽  
A. J. Kleinheksel

Abstract Purpose Debriefing is necessary for effective simulation education. The PEARLS (Promoting Excellence and Reflective Learning in Simulations) is a scripted debriefing model that incorporates debriefing best practices. It was hypothesized that student simulation performance might impact facilitator adherence to the PEARLS debriefing model. There are no published findings on the effect of student performance on debriefer behavior. Methods Third-year medical students participated in a video-recorded, formative simulation to treat a high-fidelity mannequin for an asthma exacerbation. A faculty debriefer trained in the PEARLS model evaluated student performance with a standardized rubric and conducted a recorded debriefing. Debriefing recordings were analyzed for debriefer adherence to the PEARLS model. Debriefers were assigned a debriefing score (DS) from 0 to 13; 13 was perfect adherence to the model. Definitive intervention (DI) for asthma exacerbation was defined as bronchodilator therapy. Critical actions were as follows: a focused history, heart/lung exam, giving oxygen, and giving a bronchodilator. Results Mean DS for the debriefers of students who provided DI was 8.57; 9.14 for those students who did not (P = 0.25). Mean DS for debriefers of students who completed all critical actions was 8.68; 8.52 for those students who did not (P = 0.62). Analysis of elapsed time to DI showed no relationship between the time DI was provided and DS. Conclusions Student performance had no impact on debriefer performance, suggesting the PEARLS model is an effective aid for debriefers, regardless of learner performance. These findings suggest student performance may not bias facilitators’ ability to conduct quality debriefings.


2018 ◽  
Vol 35 (3) ◽  
Author(s):  
Hong Ji ◽  
Ronghao Chen ◽  
Yong Huang ◽  
Wenqin Li ◽  
Chunhui Shi ◽  
...  

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