Fatigue and sleep quality in nurses

Fatigue is a critical issue for nurses that may lead to medical errors, degradation in performance, decreased mental acuity, and social problems. Poor sleep quality is also a contributing factor in fatigue that nurses experience. Therefore, the purpose of this study was to examine the differences in perceptions of fatigue between night-shift and day-shift nurses, as measured by scores on the Brief Fatigue Inventory (BFI) and to examine differences in sleep quality between the two groups, as measured by responses on the Pittsburgh Sleep Quality Index (PSQI). Univariate analysis of variance showed significant differences between the two groups on the BFI, with the night-shift cohort reporting higher mean scores on the BFI. Significant differences were also found between the two groups on PSQI mean scores, with the night-shift cohort reporting higher mean scores. Findings from this study suggest that night-shift nurses, compared with day-shift nurses, perceived a much higher level of fatigue and had poorer sleep quality. Further research is needed to evaluate interventions that might decrease fatigue in nurses and improve their sleep quality.     

Community YouthMapping: female youth voices through empowerment and workforce preparation

Because of poverty, many girls and young women throughout Egypt neglect their education in order to marry early, help in the fields, gain immediate employment, or carry out domestic labor to meet family needs. In Egypt, the Academy for Educational Development's Community YouthMapping (CYM) has been structured to provide a solid foundation for a systemic approach to education reform. CYM focuses on changing communities' perceptions and behavior through its youth and mechanisms to develop transferable skills to those who have traditionally been marginalized during afterschool time.     

Proteins' promise--progress and challenges in ovarian cancer proteomics

Ovarian cancer is the leading cause of gynaecological cancer death. The mortality rate of ovarian cancer could be greatly decreased if there were a screening test which was able to detect the disease at an early stage, resulting in an increased probability of cure. The most promising prospect for the early detection of ovarian cancer comes from the rapidly advancing field of clinical proteomics. An increasing number of reports on the potential clinical application of proteomics research for early detection as well as risk assessment and management of ovarian cancer are being published. Although the research is very promising, major technical challenges are still preventing new discoveries in ovarian cancer proteomics from being translated into clinical practice.     

Quasi-binomial zero-inflated regression model suitable for variables with bounded support

In recent years, a variety of regression models, including zero-inflated and hurdle versions, have been proposed to explain the case of a dependent variable with respect to exogenous covariates. Apart from the classical Poisson, negative binomial and generalised Poisson distributions, many proposals have appeared in the statistical literature, perhaps in response to the new possibilities offered by advanced software that now enables researchers to implement numerous special functions in a relatively simple way. However, we believe that a significant research gap remains, since very little attention has been paid to the quasi-binomial distribution, which was first proposed over fifty years ago. We believe this distribution might constitute a valid alternative to existing regression models, in situations in which the variable has bounded support. Therefore, in this paper we present a zero-inflated regression model based on the quasi-binomial distribution, taking into account the moments and maximum likelihood estimators, and perform a score test to compare the zero-inflated quasi-binomial distribution with the zero-inflated binomial distribution, and the zero-inflated model with the homogeneous model (the model in which covariates are not considered). This analysis is illustrated with two data sets that are well known in the statistical literature and which contain a large number of zeros.     

Death by survey: Estimating adult mortality without selection bias from sibling survival data

The widely used methods for estimating adult mortality rates from sample survey responses about the survival of siblings, parents, spouses, and others depend crucially on an assumption that, as we demonstrate, does not hold in real data. We show that when this assumption is violated so that the mortality rate varies with sibship size, mortality estimates can be massively biased. By using insights from work on the statistical analysis of selection bias, survey weighting, and extrapolation problems, we propose a new and relatively simple method of recovering the mortality rate with both greatly reduced potential for bias and increased clarity about the source of necessary assumptions.     

Acknowledgement to editorial collaborators

Acknowledgments

Acknowledgement to editorial collaborators