Assessing nursing students’ e−health literacy and foresights to global health challenges during COVID−19 restrictions: A cross-sectional study with a machine learning approach

Aim

This study aims to assess the e-health literacy and foresights to global health challenges of nursing students during the COVID19 restrictions.

Background

As the healthcare environment becomes more digitalized, understanding how nursing students perceive and prepare for global health challenges during the COVID19 restrictions and e-health literacy is crucial for customizing educational strategies to enhance their capabilities.

Design

A descriptive and correlational study design was employed.

Methods

The study included 310 nursing students from six universities in Istanbul, Turkey, encompassing both state and foundation institutions. Data were collected via online surveys, including an Information Form, a Foresight Form for Global Health Challenges and an E$-$Health Literacy Scale. The study extended beyond conventional statistical analysis by incorporating a tree-based ML model, specifically a Random Forest classifier, to identify complex patterns and relationships affecting e$-$health literacy and global health perceptions.

Results

The analysis indicated that e$-$health literacy levels among nursing students are significantly shaped by their academic year, participation in global health courses and engagement with international health organizations. ML techniques pinpointed the ability to discern high$-$quality online health resources as a pivotal skill, emphasizing the need for nursing curricula to focus on advanced critical evaluation skills.

Conclusions

The findings stress the necessity of integrating critical evaluation and information$-$seeking skills into nursing education to equip students for the complexities of a globalized health landscape.