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Aims and objectives This study examines the relationship between hospital work environments and job satisfaction, job-related burnout and intention to leave among nurses in Guangdong province, China. Background The nursing shortage is an urgent global problem and also of concern in China. Studies in Western countries have shown that better work environments are associated with higher nurse satisfaction and lower burnout, thereby improving retention and lowering turnover rates. However, there is little research on the relationship between nurse work environments and nurse outcomes in China. Design This is a cross-sectional study. Survey data were collected from 1104 bedside nurses in 89 medical, surgical and intensive care units in 21 hospitals across the Guangdong province in China. Methods Stratified convenience sampling was used to select hospitals, and systematic sampling was used to select units. All staff nurses working on participating units were surveyed. The China Hospital Nurse Survey, including the Practice Environment Scale of the Nursing Work Index and Maslach Burnout Inventory, was employed to collect data from nurses. Statistical significance level was set at 0·05. Results Thirty-seven per cent of the nurses experienced high burnout, and 54% were dissatisfied with their jobs. Improving nurses’ work environments from poor to better was associated with a 50% decrease in job dissatisfaction and a 33% decrease in job-related burnout among nurses. Conclusion Burnout and job dissatisfaction are high among hospital nurses in Guangdong province, China. Better work environments for nurses were associated with decreased job dissatisfaction and job-related burnout, which may successfully address the nursing shortage in China. Relevance to clinical practice The findings of this study indicate that improving work environments is essential to deal with the nursing shortage; the findings provide motivation for nurse managers and policy makers to improve work environments of hospital nurses in China.

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MotivationThe increasingly large amount of free, online biological text makes automatic interaction extraction correspondingly attractive. Machine learning is one strategy that works by uncovering and using useful properties that are implicit in the text. However these properties are usually not reported in the literature explicitly. By investigating specific properties of biological text passages in this paper, we aim to facilitate an alternative strategy, the use of text empirics, to support mining of biomedical texts for biomolecular interactions. We report on our application of this approach, and also report some empirical findings about an important class of passages. These may be useful to others who may also wish to use the empirical properties we describe.ResultsWe manually analyzed syntactic and semantic properties of sentences likely to describe interactions between biomolecules. The resulting empirical data were used to design an algorithm for the PathBinder system to extract biomolecular interactions from texts. PathBinder searches PubMed for sentences describing interactions between two given biomolecules. PathBinder then uses probabilistic methods to combine evidence from multiple relevant sentences in PubMed to assess the relative likelihood of interaction between two arbitrary biomolecules. A biomolecular interaction network was constructed based on those likelihoods.ConclusionThe text empirics approach used here supports computationally friendly, performance competitive, automatic extraction of biomolecular interactions from texts.Availabilityhttp://www.metnetdb.org/pathbinder.

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