Martín-Rodríguez Francisco, Castro Villamor Miguel A, López-Izquierdo Raúl, Portillo Rubiales Raquel M, Ortega Guillermo J, Sanz-García Ancor
Faculty of Medicine, Valladolid University, Valladolid, Spain; Advanced Life Support, Emergency Medical Services, Valladolid, Spain.
Faculty of Medicine, Valladolid University, Valladolid, Spain; Community Health Center, La Cistérniga, Valladolid, Spain.
Nurse Educ Today. 2021 Mar;98:104774. doi: 10.1016/j.nedt.2021.104774. Epub 2021 Jan 19.
High-fidelity clinical simulation has implied a revolution in health science training. Despite its benefits, some drawbacks could hinder the learning process, especially the anxiety produced during such scenarios.
The aim of the present work is to develop a predictive model capable of determining which students will present high levels of anxiety.
We performed a randomized, sham-controlled, blinded trial in which students were randomly assigned to four scenarios and played one of two possible roles.
Before and after the simulation we assessed the anxiety level along with physiological and analytical parameters. The main analyzed outcome was an increase of ≥25% in anxiety compared with baseline.
The type of scenario or the role played had no effect on anxiety. The predictive model presented an Area Under the Receiver Operating Characteristics of 0.798 (95% CI: 0.69-0.90; p < 0.001), with age and systolic blood pressure being protective factors against anxiety.
Our results showed that the anxiety level developed during simulation could be predicted. The application of this predictive model when associated to appropriate techniques to deal with increased anxiety levels could improve the learning process of medical students during simulations.
高保真临床模拟意味着健康科学培训的一场革命。尽管有其益处，但一些缺点可能会阻碍学习过程，尤其是在此类场景中产生的焦虑情绪。
本研究的目的是开发一种能够确定哪些学生将表现出高度焦虑的预测模型。
我们进行了一项随机、假对照、双盲试验，学生被随机分配到四种场景中，并扮演两种可能角色中的一种。
在模拟前后，我们评估了焦虑水平以及生理和分析参数。主要分析结果是与基线相比焦虑增加≥25%。
场景类型或所扮演的角色对焦虑没有影响。预测模型的受试者工作特征曲线下面积为0.798（95%置信区间：0.69 - 0.90；p < 0.001），年龄和收缩压是焦虑的保护因素。
我们的结果表明，模拟过程中产生的焦虑水平是可以预测的。将此预测模型与适当的技术相结合，以应对焦虑水平的增加，可能会改善医学生在模拟过程中的学习过程。
