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

IF 3.3 3区 医学 Q1 NURSING Nurse Education in Practice Pub Date : 2024-11-01 DOI:10.1016/j.nepr.2024.104179
Cisem KUPCU , Gonul BODUR , Aycan KUCUKKAYA , Polat GOKTAS
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Abstract

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 EHealth 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 ehealth literacy and global health perceptions.

Results

The analysis indicated that ehealth 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 highquality 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 informationseeking skills into nursing education to equip students for the complexities of a globalized health landscape.
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在 COVID-19 限制期间评估护理专业学生的电子健康知识和对全球健康挑战的预见:采用机器学习方法的横断面研究。
目的:本研究旨在评估护理专业学生在 COVID19 限制期间的电子健康素养和对全球健康挑战的预见:背景:随着医疗保健环境日益数字化,了解护理专业学生如何看待 COVID19 限制期间的全球健康挑战并做好准备,以及电子健康素养对于定制教育策略以提高他们的能力至关重要:设计:采用描述性和相关性研究设计:研究对象包括来自土耳其伊斯坦布尔六所大学的 310 名护理专业学生,其中既有国立院校也有基金会院校。数据通过在线调查收集,包括信息表、全球健康挑战展望表和电子健康素养量表。该研究超越了传统的统计分析,采用了基于树的 ML 模型,特别是随机森林分类器,以确定影响电子健康素养和全球健康观念的复杂模式和关系:结果:分析表明,护理专业学生的电子健康素养水平在很大程度上取决于他们的学年、参与全球健康课程的情况以及与国际健康组织的联系。ML技术指出,辨别高质量在线健康资源的能力是一项关键技能,这强调了护理课程需要注重高级批判性评估技能:研究结果强调,有必要将批判性评价和信息搜索技能纳入护理教育,使学生能够应对全球化卫生环境的复杂性。
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来源期刊
CiteScore
5.40
自引率
9.40%
发文量
180
审稿时长
51 days
期刊介绍: Nurse Education in Practice enables lecturers and practitioners to both share and disseminate evidence that demonstrates the actual practice of education as it is experienced in the realities of their respective work environments. It is supportive of new authors and will be at the forefront in publishing individual and collaborative papers that demonstrate the link between education and practice.
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