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This study examined the moderating effects of income inequality and nurse–patient relationships on the association between occupational stress and nurse turnover intentions in large urban hospitals in China, providing evidence for developing targeted retention strategies.
A cross-sectional study.
Data from 13,298 nurses in 46 hospitals in Xi'an, China (October–December 2023) were analysed using hierarchical regression to assess associations between occupational stress, organisational and professional turnover intentions and the moderating roles of the expected income achievement rate (calculated as [actual/expected income] × 100%) and nurse–patient relationship quality.
Eighty-three percent of nurses reported moderate-to-severe occupational stress. Compared to nurses experiencing mild stress, those with moderate/severe stress demonstrated significantly higher organisational and professional turnover intentions. After adjusting for covariates, significant interaction effects were observed. Higher expected income achievement rate showed a modest but significant moderating effect, associated with reduced turnover intentions. While the nurse–patient relationship also moderated this relationship, its protective effect was attenuated under conditions of severe stress. Despite small effect sizes, the consistent patterns and theoretical coherence of these interactions warrant further investigation.
Occupational stress significantly predicts nurse turnover intentions in urban Chinese hospitals, with income inequality and nurse–patient relationship quality serving as modifiable moderating factors. Interventions should integrate equitable compensation, nurse–patient relationship enhancement programmes and stress management initiatives.
This study demonstrates that equitable income consistently buffers the effects of occupational stress on nurse turnover, while nurse–patient relationships show stress-level-dependent moderation. By implementing region-specific compensation benchmarks and structured communication training, healthcare policymakers can effectively address economic security and relational care quality in workforce stabilisation.
The study has been reported following the STROBE guidelines.
No patient or public contribution.
To develop a deep learning-based smart assessment model for pressure injury surface.
Exploratory analysis study.
Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.
From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm2).
The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.
This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.
The STROBE checklist guided study reporting.
Patients provided image resources for model training.
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