Deep learning and its associated factors among Chinese nursing undergraduates: A cross-sectional study

Abstract

Background

Adequate professional preparation of nursing undergraduates is conducive to developing health care careers. Deep learning is important for enhancing nursing competencies and the overall quality of students. However, limited research has been conducted to explore deep learning and its associated factors for students in higher nursing education.

Objective

To describe the level of deep learning and explore its associated factors among Chinese nursing undergraduates.

Design

A cross-sectional study.

Setting

This study was conducted at a medical university in Anhui Province, China.

Participants

Convenience sampling was used to survey 271 nursing undergraduates between July and September 2023.

Methods

The survey included questions about general information, deep learning, and critical thinking disposition. Nonparametric tests were used to distinguish the intergroup differences. Correlations were evaluated using Spearman's rank correlation analysis. Hierarchical multiple regression analysis was performed to determine the influencing factors.

Results

The deep learning score of the nursing undergraduates was 3.82 (3.56, 4.00). Hierarchical multiple regression analysis revealed that gender (β = 0.10, P = 0.044), experience as a student leader (β = 0.10, P = 0.049), and critical thinking disposition (β = 0.60, P = 0.000) significantly impacted deep learning. All the variables explained 41.1 % of the total mean score variance for deep learning.

Conclusion

Chinese nursing undergraduates showed upper-middle levels of deep learning. Gender, experience as a student leader, and critical thinking disposition were significantly associated factors of deep learning. Nursing educators should provide targeted interventions for deep learning to facilitate the professional competencies of these students.