Sleep disturbances and subjective sleep quality emerged as the most central components linking resilience and multidimensional sleep quality in nurses, suggesting interventions targeting these core domains may offer a pathway to strengthen nurses' well-being.
Key Findings
Results
Sleep duration and sleep efficiency showed the strongest positive association among sleep quality components in the network model.
Study used the Pittsburgh Sleep Quality Index (PSQI) seven-component structure to assess multidimensional sleep quality
Network was estimated using EBICglasso regularization among 697 registered nurses from tertiary hospitals in Southwest China
Sleep duration and sleep efficiency formed the strongest positive edge in the network
The cross-sectional correlational design limits causal interpretation
Results
Sleep disturbances and daytime dysfunction showed the strongest positive association after sleep duration and sleep efficiency.
Both sleep disturbances and daytime dysfunction are components of the PSQI
This association was identified within the psychometric network model using EBICglasso regularization
Sample consisted of N = 697 registered nurses from Southwest China tertiary hospitals
These two nodes were also among the most interconnected in the broader sleep component network
Results
The strongest negative association in the network was between sleep latency and subjective sleep quality.
Sleep latency and subjective sleep quality are two of the seven PSQI components
A negative edge between these nodes indicates that higher sleep latency was inversely associated with better subjective sleep quality in the network structure
This relationship was identified through EBICglasso regularized partial correlation network estimation
Network accuracy and stability were assessed using bootstrap procedures
Results
Sleep disturbances and subjective sleep quality were identified as the most central nodes in the network linking resilience and multidimensional sleep quality.
Centrality was assessed using strength centrality and bridge centrality metrics
Bootstrap analyses supported acceptable stability with a case-stability coefficient (CS) of 0.52
These two components were described as 'the most central nodes' in the network
Their centrality suggests they serve as key linking points between resilience (measured by CD-RISC-10) and overall sleep quality
Results
Network structure did not differ significantly by prior experience of patient attacks.
Group differences were examined using the Network Comparison Test (NCT) with false-discovery-rate correction
Patient attack experience was examined as a potential moderator of the resilience-sleep quality network
No statistically significant difference in network structure was found between nurses who had and had not experienced patient attacks
This finding was based on the full sample of N = 697 registered nurses
Methods
Resilience among nurses was assessed using the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) in a cross-sectional sample of 697 registered nurses from tertiary hospitals in Southwest China.
The study used a cross-sectional correlational design
Participants were registered nurses from tertiary hospitals in Southwest China
Total sample size was N = 697
Sleep quality was assessed using the seven Pittsburgh Sleep Quality Index (PSQI) components alongside the CD-RISC-10 resilience measure
What This Means
This research suggests that among nurses working in tertiary hospitals in Southwest China, specific aspects of sleep quality are closely connected to one another and to psychological resilience. Using a statistical technique called network analysis, the researchers mapped out how seven different dimensions of sleep quality — including how long nurses sleep, how easily they fall asleep, how often they wake up, and how tired they feel during the day — relate to each other and to nurses' ability to bounce back from stress (resilience). The strongest connections found were between sleep duration and sleep efficiency, and between nighttime sleep disturbances and daytime tiredness.
Two dimensions of sleep stood out as particularly important 'hub' nodes in the network: sleep disturbances (such as waking up during the night) and overall subjective sleep quality (how nurses rate their own sleep). These two factors appeared most central in connecting resilience to the broader pattern of sleep health, suggesting they may be especially important targets for workplace wellness programs. The study also found that whether or not a nurse had previously been attacked by a patient did not significantly change these network relationships.
This research suggests that hospital systems looking to support nurse well-being might gain the most benefit by focusing interventions on reducing sleep disturbances and improving nurses' overall sense of sleep quality, rather than targeting all sleep dimensions equally. Because the study was cross-sectional — meaning it captured a single point in time — it cannot determine whether poor sleep reduces resilience, low resilience worsens sleep, or both. Future longitudinal research would be needed to understand the direction of these relationships.
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Lu W, Huang C, Wei M, Bai C, Fan X, Wu D. (2026). A Cross-Sectional Network Analysis of Psychological Resilience and Multidimensional Sleep Quality in Nurses.. International nursing review. https://doi.org/10.1111/inr.70183