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This study aimed to investigate the network structural characteristics of self-efficacy and professional resilience among emergency nurses, identify core nodes within the network, and elucidate the key interactive mechanisms between these constructs.
Descriptive cross-sectional study.
A multi-center cross-sectional study was conducted from January to February 2025, involving 612 emergency nurses from 20 hospitals in Sichuan, China. Data were collected using a self-administered demographic questionnaire, the General Self-Efficacy Scale, and the Chinese Emergency Nurse Professional Resilience Tool. An adjacent network integrating professional resilience and self-efficacy was developed. Key covariates—including title, position, tenure in the hospital or emergency department, education, and exposure to workplace violence—were included as control variables. Network precision and stability were evaluated using the correlation stability coefficient and confidence intervals for edge weights. To further test the robustness of the network model, sensitivity analyses were performed by adding each significant covariate to the original model. The Network Comparison Test was then used to compare the covariate-adjusted and unadjusted networks, assessing differences in network structure, overall strength, and edge weights.
The analysis identified S9 as the central node in the network. The overall network showed satisfactory stability and precision. The Network Comparison Test showed no significant differences in network structure or global strength between the adjusted and unadjusted models, indicating that the network was stable and robust to covariate adjustment.
This network analysis revealed the interaction mechanisms between self-efficacy and professional resilience among emergency nurses through contemporaneous network modelling and identified S9 as the core node, suggesting that this coping strategy plays a key role in regulating psychological resources. The overall network demonstrated good stability and precision, with no statistically significant differences between the adjusted and unadjusted models according to the Network Comparison Test. These findings indicate that the network structure was robust to covariate adjustment and provide a reference for developing and optimising intervention strategies to enhance professional resilience among emergency nurses.
For Emergency Nurses and the Management of Emergency Nursing Practice: What problem does this study address?
This study addresses the gap in understanding how self-efficacy and occupational resilience interact in emergency nurses under high-stress conditions.
A contemporaneous network analysis revealed a central node linking self-efficacy and resilience, highlighting key pathways in their mutual influence.
The findings offer practical guidance for emergency nursing management, supporting the development of targeted strategies to strengthen nurses' resilience, enhance professional competence, and improve the quality of emergency care.
This study is reported using the STROBE guidelines.
No Patient or Public Involvement: This study did not include patient or public involvement in its design, conduct, or reporting.
To combine the Job Demand-Resource (JD-R) model with machine learning (ML) techniques to identify the key factors affecting job burnout (JB) among Chinese nurses.
A Cross-Sectional Study.
This study utilised a stratified sampling method to recruit 3449 eligible nurses from eight cities in Shandong Province between June and December 2021. After data cleaning, 2998 valid samples were retained. The dataset was randomly split into a training set (75%) and a test set (25%). The Boruta algorithm was used to select relevant variables for model construction. Six-millilitre models were compared using cross-validation, with mean absolute error (MAE), root mean square error (RMSE) and R-squared (R 2) used to select the best model. The Shapley Additive Explanation (SHAP) method was used to identify key predictors of JB.
The average JB score among nurses was (32.88 ± 11.45). Among the 20 variables, 17 were identified by the Boruta algorithm as strongly associated with JB, including 7 job demand-related variables and 10 job resource-related variables. After comparing 6-ml models, the Random Forest was identified as the optimal model (MAE = 6.56, RMSE = 8.86, R 2 = 0.63). SHAP analysis further revealed the importance ranking of these 17 variables and identified four key predictors: psychological distress (SHAP = 4.07), perceived organisational support (SHAP = 2.03), emotional intelligence (SHAP = 1.81) and D-type personality (SHAP = 1.73).
By integrating the JD-R model framework, ML algorithms proved effective in identifying critical predictors of nurses' JB. SHAP analysis identified four primary determinants: psychological distress, perceived organisational support, emotional intelligence and D-type personality. These findings provide novel insights for nursing administrators to optimise intervention strategies.
Not applicable.
This study did not include patient or public involvement in its design, conduct or reporting.
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