Abstract
High-fidelity simulation (HFS) is a technology-based educational approach performed in a realistic and safe environment. It is a powerful tool for nursing education, especially in critical care. Literature results are heterogeneous but confirm the power of this teaching methodology in improving learning outcomes. However, little is known about the factors associated with learning gains in postgraduate students who have different characteristics and expectations when compared to undergraduate ones. In this study factors associated with learning gain and its magnitude were explored. A retrospective multi-cohort study was conducted considering four cohorts of Intensive and Critical Care Nursing Students. Students were involved in HFS learning experience based on a critical care scenario of acute respiratory failure. Immediately after, Self-Confidence, Self-Efficacy, and Satisfaction were evaluated. Descriptive statistics were used to illustrate the demographic characteristics of the sample. Explorative univariate logistic and linear regression analyses were carried out to examine the relationships between data related to the profile of students and learning gain and its magnitude. A total of 79 students were included in the study. Self-Confidence and Self-Efficacy improved immediately after the HFS session (+2.8, p < 0.001; +1.5, p < 0.001). Satisfaction level was at 23.5 points out of 25. Univariate logistic regression showed satisfaction as a predictor of both Self-Confidence and Self-Efficacy. The linear regression model showed age and Satisfaction significantly predicted the magnitude learning gains for both Self-Confidence and Self-Efficacy. The multivariate analysis confirmed the predictive power on the magnitude of learning gains of age and Satisfaction.
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Masotta, V. et al. (2023). Factors Associated with Intensive and Critical Care Nursing Students’ Learning Gains Exposed to High-Fidelity Simulation Training. In: Kubincová, Z., Caruso, F., Kim, Te., Ivanova, M., Lancia, L., Pellegrino, M.A. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International Conference. MIS4TEL 2023. Lecture Notes in Networks and Systems, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-031-42134-1_25
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