Academic resilience of nursing students during COVID-19: An analysis using machine learning methods

Abstract

Aim: This cross-sectional study investigates the factors that contribute to academic resilience among nursing students during COVID-19 pandemic.

Design: A cross-sectional study.

Methods: A survey was conducted in a general hospital between November and December 2022. The Nursing Student Academic Resilience Inventory (NSARI) model was used to assess the academic resilience of 96 nursing students. The Boruta method was then used to identify the core factors influencing overall academic resilience, and rough set analysis was used to analyse the behavioural patterns associated with these factors.

Results: Attributes were categorised into three importance levels. Three statistically significant attributes were identified ("I earn my patient's trust by making suitable communication," "I receive support from my instructors," and "I try to endure academic hardship") based on comparison with shadow attributes. The rough set analysis showed nine main behavioural patterns. Random forest, support vector machines, and backpropagation artificial neural networks were used to test the performance of the model, with accuracies ranging from 73.0% to 76.9%.

Conclusion: Our results provide possible strategies for improving academic resilience and competence of nursing students.

Keywords: cross‐sectional study; machine learning; nursing; nursing education; resilience.