AI Insight
This study analyzed sleep quality patterns among 611 emergency department nurses from 12 hospitals in China and identified four distinct profiles ranging from severely disturbed sleep (8.0%) to good sleep quality (50.2%). Higher psychological resilience, along with factors like age, work experience, income, and night shift frequency, were significantly associated with better sleep quality profiles. The research suggests that emergency nurses experience varied sleep disturbances that could be addressed through targeted interventions based on their specific sleep profile characteristics.
Why it matters
The findings provide evidence for developing personalized interventions to improve sleep quality among emergency nurses, a population facing chronic high-stress conditions. By identifying distinct sleep profiles and their associated factors, hospitals can implement more effective, subgroup-specific strategies to enhance nurse wellbeing and potentially improve patient care quality.
Understand the Science
ObjectiveEmergency department nurses are consistently exposed to high-intensity and high-pressure working conditions, leading to poor sleep quality. Psychological resilience can help alleviate the negative impacts of work stress. However, few studies have explored how resilience varies across distinct subgroups defined by different sleep profiles. This study employed latent profile analysis to identify distinct sleep-quality profiles among emergency department nurses and explored their association with psychological resilience to provide a basis for targeted interventions.MethodsFrom October to December 2025, 611 emergency nurses were recruited from 12 tertiary hospitals in Shandong through convenience sampling. Data were collected using a sociodemographic questionnaire, the Connor–Davidson Resilience Scale, and the Pittsburgh Sleep Quality Index. Sleep quality profiles were determined through latent profile analysis, and associated factors were analyzed using multiple logistic regression.ResultsEmergency department nurses exhibited generally poor sleep quality, which was classified into four distinct profiles: “severely disturbed sleep” (8.0%), “sleep onset difficulty-sleep medication use” (15.1%), “mildly disturbed sleep” (26.7%), and “good sleep quality” (50.2%). Psychological resilience, age, work experience, monthly income, and monthly night shift number were significantly associated with these sleep quality profiles (p < 0.05).ConclusionEmergency department nurses exhibit distinct sleep quality profiles. Targeted interventions addressing the specific factors associated with each subgroup could improve their sleep outcomes.