Nurse burnout in China: a questionnaire survey on staffing, job satisfaction, and quality of care Aim The investigators examined how nurse staffing affects nurse job satisfaction and quality of care. Background Inadequate nurse staffing is a worldwide issue with profound effects on nurse job satisfaction and quality of care. Few studies have examined the relationship between nurse staffing and job satisfaction and quality of care in China.Method A cross-sectional design was adopted, wherein 873 nurses were surveyed on demographics, nurse staffing, job-related burnout, job dissatisfaction, intent to leave, and quality of care.Result The median patient-nurse ratio was five; 45.1% nurses reported high levels of job-related burnout, and 55.6%, job dissatisfaction. In adjusted regression models, patient-nurse ratios of four or less were related to a decrease in the odds of job dissatisfaction (odds ratio 0.55, 95% confidence interval 0.36-0.85) and increase in the odds of quality of care (odds ratio 1.78, 95% confidence interval 1.02-2.82). Conclusion Nurse staffing is associated with job dissatisfaction and quality of care. Implications for nursing management Nurse managers should maintain an adequate level of nurse staffing, referring to the patient-nurse ratio. They should create new initiatives to increase job satisfaction among nurses and to evaluate their effects.

With the exhaustion of IPv4 addresses, research on the adoption, deployment, and prediction of IPv6 networks becomes more and more significant. This paper analyzes the IPv6 traffic of two campus networks in Shanghai, China. We first conduct a series of analyses for the traffic patterns and uncover weekday/weekend patterns, the self-similarity phenomenon, and the correlation between IPv6 and IPv4 traffic. On weekends, traffic usage is smaller than on weekdays, but the distribution does not change much. We find that the self-similarity of IPv4 traffic is close to that of IPv6 traffic, and there is a strong positive correlation between IPv6 traffic and IPv4 traffic. Based on our findings on traffic patterns, we propose a new IPv6 traffic prediction model by combining the advantages of the statistical and deep learning models. In addition, our model would extract useful information from the corresponding IPv4 traffic to enhance the prediction. Based on two real-world datasets, it is shown that the proposed model outperforms eight baselines with a lower prediction error. In conclusion, our approach is helpful for network resource allocation and network management.

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