Background:Burnout is a problem that impacts on the staff management costs and on the patient care quality.Objective:This work aimed to investigate some psychosocial factors related to burnout. Specifically, we explored the sample characteristics for moderate/high emotional exhaustion, cynicism and professional inefficacy, as well as the relationship between both working and environmental variables and burnout.Method:A cross-sectional study involving 307 nurses from one Italian hospital was carried out. A self-reported questionnaire was used to collect data. Data analysis was performed by using SPSS 19.0.Results:The results showed that there was a significant difference between nurses with low and moderate/high burnout in all the three components in almost all the examined organizational variables. In addition, we found that the aspects of working life had a significant impact on the three dimensions of burnout.Conclusions: The findings of this study not only can provide useful basis for future research in the field, but also can offer practical suggestions for improving nursing practice and promote effective workplace, thus reducing the risk burnout among nurses.

Inter-subjects' variability in functional brain networks has been extensively investigated in the last few years. In this context, unveiling subject-specific characteristics of EEG features may play an important role for both clinical (e.g., biomarkers) and bio-engineering purposes (e.g., biometric systems and brain computer interfaces). Nevertheless, the effects induced by multi-sessions and task-switching are not completely understood and considered. In this work, we aimed to investigate how the variability due to subject, session and task affects EEG power, connectivity and network features estimated using sourcereconstructed EEG time-series. Our results point out a remarkable ability to identify subject-specific EEG traits within a given task together with striking independence from the session. The results also show a relevant effect of task-switching, which is comparable to individual variability. This study suggests that power and connectivity EEG features may be adequate to detect stable (over-time) individual properties within predefined and controlled tasks.

Inter-subjects' variability in functional brain networks has been extensively investigated in the last few years. In this context, unveiling subject-specific characteristics of EEG features may play an important role for both clinical (e.g., biomarkers) and bio-engineering purposes (e.g., biometric systems and brain computer interfaces). Nevertheless, the effects induced by multi-sessions and task-switching are not completely understood and considered. In this work, we aimed to investigate how the variability due to subject, session and task affects EEG power, connectivity and network features estimated using sourcereconstructed EEG time-series. Our results point out a remarkable ability to identify subject-specific EEG traits within a given task together with striking independence from the session. The results also show a relevant effect of task-switching, which is comparable to individual variability. This study suggests that power and connectivity EEG features may be adequate to detect stable (over-time) individual properties within predefined and controlled tasks.