Nursing students' blended learning satisfaction, self-efficacy, and work readiness: A structural equation modeling study

Aim: To determine the relationship between blended learning satisfaction with self-efficacy and the work readiness of nursing graduates. Background: Blended learning is an educational approach to ensuring quality nursing education during the COVID-19 pandemic. However, examining the nursing students' blended learning satisfaction and its association with self-efficacy and work readiness remains underreported during the pandemic. Design: A descriptive-correlational design using a structural equation modeling (SEM) approach. Methods: This study was participated by 208 nursing graduates recruited through consecutive sampling. Four self-report tools were used to collect data (Student Learning Satisfaction in a Blended Learning Satisfaction Tool [BLST], Nursing Self-Efficacy Tool [NSET], and Nursing Work Readiness Tool [NWRT]). Data analysis included descriptive statistics and SEM. Results: The results indicated high levels of blended learning satisfaction (M = 2.88; SD = 0.45), self-efficacy (M = 3.25; SD = 0.35), and work readiness (M = 3.34; SD = 0.34). The emerging model demonstrated acceptable model fit indices, with self-efficacy increasing with blended learning satisfaction (p = 0.16) and a direct impact on work readiness (p = 0.73). In addition, blended learning satisfaction does not directly influence work readiness (p =-0.03) but has a mediating effect on work readiness through self-efficacy (p = 0.009). Conclusion: Nursing students who underwent blended learning in their senior year reported high levels of blended learning satisfaction, self-efficacy, and work readiness. Nursing students in a blended learning environment have the potential to be work-ready if they consider themselves self-efficacious and have the support of a suitable blended learning set-up. (c) 2024 Organization for Associate Degree Nursing. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.