Nurse education today · 2026
Technostress and learning burnout among nursing students: The protective roles of academic self-efficacy and psychological resilience - A cross-sectional study.
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Chen Lei, Liu Yukun, Chen Si, Zhang Wenjing
Аннотация
BACKGROUND: The rapid integration of digital modalities within contemporary nursing curricula has exposed student cohorts to unique psychological strains, prominently manifested as technostress. While technostress is known to accelerate learning burnout, the complex boundary conditions and moderating mechanisms through which internal cognitive assets-specifically academic self-efficacy-alter or shape this negative relationship remain insufficiently quantified.
OBJECTIVE: This study established a hierarchical predictive framework and moderation model to quantify the impact of technostress on learning burnout among nursing students, while evaluating the moderating role and continuous boundary limits of academic self-efficacy and psychological resilience.
METHODS: A cross-sectional survey design was administered to 1277 nursing students. Analytical validation included Pearson correlations, multi-stage hierarchical linear regression, Model 1 moderation analysis, simple slope tests, and Johnson-Neyman (J-N) continuous significance boundary region calculations, controlling for baseline demographic covariates.
RESULTS: Technostress directly predicted learning burnout (β = 0.383, p < .001, ΔR2 = 0.144), while psychological resilience (β = -0.349) and academic self-efficacy (β = -0.124) provided significant protective variance (ΔR2 = 0.203, p < .001). Crucially, academic self-efficacy moderated the technostress-burnout relationship (b = 0.0220, p < .001, ΔR2 = 0.0192). Johnson-Neyman analysis confirmed this continuous moderation remained significant across 97.73% of the student continuum (mean-centered threshold at -27.62).
CONCLUSION: Beyond direct protective benefits, academic self-efficacy acts as a key moderator that shapes the impact of technostress on learning burnout. Because these foundational boundary dynamics reflect universal digital learning challenges across contemporary nursing education, institutional interventions globally must couple structural digital optimization with targeted self-efficacy enhancement to maximize psychological protection.
OBJECTIVE: This study established a hierarchical predictive framework and moderation model to quantify the impact of technostress on learning burnout among nursing students, while evaluating the moderating role and continuous boundary limits of academic self-efficacy and psychological resilience.
METHODS: A cross-sectional survey design was administered to 1277 nursing students. Analytical validation included Pearson correlations, multi-stage hierarchical linear regression, Model 1 moderation analysis, simple slope tests, and Johnson-Neyman (J-N) continuous significance boundary region calculations, controlling for baseline demographic covariates.
RESULTS: Technostress directly predicted learning burnout (β = 0.383, p < .001, ΔR2 = 0.144), while psychological resilience (β = -0.349) and academic self-efficacy (β = -0.124) provided significant protective variance (ΔR2 = 0.203, p < .001). Crucially, academic self-efficacy moderated the technostress-burnout relationship (b = 0.0220, p < .001, ΔR2 = 0.0192). Johnson-Neyman analysis confirmed this continuous moderation remained significant across 97.73% of the student continuum (mean-centered threshold at -27.62).
CONCLUSION: Beyond direct protective benefits, academic self-efficacy acts as a key moderator that shapes the impact of technostress on learning burnout. Because these foundational boundary dynamics reflect universal digital learning challenges across contemporary nursing education, institutional interventions globally must couple structural digital optimization with targeted self-efficacy enhancement to maximize psychological protection.