Navigating the future: unveiling new facets of nurse work engagement - Yini Zhang; Ruolin Qiu; Yuezhong Wang; Zhihong Ye (BMC Nursing)
Abstract Objective This study investigates the influence of structural empowerment and psychological capital on nurse work engagement within the context of rising healthcare demands and nursing staff shortages. Methods A cross-sectional descriptive study involving 778 registered nurses from six tertiary hospitals in Hangzhou, China, was conducted. Data were collected using multiple tools, including a demographic questionnaire, the CWEQ-II (Conditions for Work Effectiveness Questionnaire II), the PCQ (Psychological Capital Questionnaire), and the UWES-9 (Utrecht Work Engagement Scale-9). SPSS 27.0 was used for Pearson correlation and regression analyses, while structural equation modeling (SEM) in AMOS was employed to explore relationships among variables. Model fit was evaluated using chi-square, CFI, AGFI, and RMSEA indices. Results Structural empowerment and psychological capital were significantly and positively correlated with nurses’ work engagement. Regression analysis indicated that structural empowerment (support, resources, opportunity, and information) and psychological capital (optimism, resilience, self-efficacy, and hope) were significant positive predictors of work engagement (p < 0.01), jointly accounting for 69% of its variance. SEM analysis further revealed that structural empowerment indirectly influenced work engagement through psychological capital, with significant path coefficients (P < 0.001) and a good model fit (χ²/df = 3.727, P = 0.000, RMSEA = 0.059). Conclusion Structural empowerment and psychological capital are crucial factors in enhancing nurse work engagement, effectively supporting nurses’ workplace performance. Management should focus on fostering psychological capital and enhancing structural empowerment to improve care quality and job satisfaction. This study provides empirical evidence for nursing management practice and suggests that future research should explore dynamic relationships among these variables in various populations and settings.