Working conditions and Work-Family Conflict in German hospital physicians: psychosocial and organisational predictors and consequences

<p>Abstract</p> <p>Background</p> <p>Germany currently experiences a situation of major physician attrition. The incompatibility between work and family has been discussed as one of the major reasons for the increasing departure of German physicians for non-clinical occupations or abroad. This study investigates predictors for one particular direction of Work-Family Conflict – namely work interfering with family conflict (WIF) – which are located within the psychosocial work environment or work organisation of hospital physicians. Furthermore, effects of WIF on the individual physicians' physical and mental health were examined. Analyses were performed with an emphasis on gender differences. Comparisons with the general German population were made.</p> <p>Methods</p> <p>Data were collected by questionnaires as part of a study on <it>Psychosocial work hazards and strains of German hospital physicians </it>during April–July 2005. Two hundred and ninety-six hospital physicians (response rate 38.9%) participated in the survey. The Copenhagen Psychosocial Questionnaire (COPSOQ), work interfering with family conflict scale (WIF), and hospital-specific single items on work organisation were used to assess WIF, its predictors, and consequences.</p> <p>Results</p> <p>German hospital physicians reported elevated levels of WIF (mean = 74) compared to the general German population (mean = 45, <it>p </it>< .01). No significant gender difference was found. Predictors for the WIF were lower age, high quantitative demands at work, elevated number of days at work despite own illness, and consequences of short-notice changes in the duty roster. Good sense of community at work was a protective factor. Compared to the general German population, we observed a significant higher level of quantitative work demands among hospital physicians (mean = 73 vs. mean = 57, <it>p </it>< .01). High values of WIF were significantly correlated to higher rates of personal burnout, behavioural and cognitive stress symptoms, and the intention to leave the job. In contrast, low levels of WIF predicted higher job satisfaction, better self-judged general health status, better work ability, and higher satisfaction with life in general. Compared to the German general population, physicians showed significantly higher levels of individual stress and quality of life as well as lower levels for well-being. This has to be judged as an alerting finding regarding the state of physicians' health.</p> <p>Conclusion</p> <p>In our study, work interfering with family conflict (WIF) as part of Work-Family Conflict (WFC) was highly prevalent among German hospital physicians. Factors of work organisation as well as factors of interpersonal relations at work were identified as significant predictors for WIF. Some of these predictors are accessible to alteration by improving work organisation in hospitals.</p

G-CSF maintains controlled neutrophil mobilization during acute inflammation by negatively regulating CXCR2 signaling

Cytokine-induced neutrophil mobilization from the bone marrow to circulation is a critical event in acute inflammation, but how it is accurately controlled remains poorly understood. In this study, we report that CXCR2 ligands are responsible for rapid neutrophil mobilization during early-stage acute inflammation. Nevertheless, although serum CXCR2 ligand concentrations increased during inflammation, neutrophil mobilization slowed after an initial acute fast phase, suggesting a suppression of neutrophil response to CXCR2 ligands after the acute phase. We demonstrate that granulocyte colony-stimulating factor (G-CSF), usually considered a prototypical neutrophil-mobilizing cytokine, was expressed later in the acute inflammatory response and unexpectedly impeded CXCR2-induced neutrophil mobilization by negatively regulating CXCR2-mediated intracellular signaling. Blocking G-CSF in vivo paradoxically elevated peripheral blood neutrophil counts in mice injected intraperitoneally with Escherichia coli and sequestered large numbers of neutrophils in the lungs, leading to sterile pulmonary inflammation. In a lipopolysaccharide-induced acute lung injury model, the homeostatic imbalance caused by G-CSF blockade enhanced neutrophil accumulation, edema, and inflammation in the lungs and ultimately led to significant lung damage. Thus, physiologically produced G-CSF not only acts as a neutrophil mobilizer at the relatively late stage of acute inflammation, but also prevents exaggerated neutrophil mobilization and the associated inflammation-induced tissue damage during early-phase infection and inflammation

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Work characteristics and determinants of job satisfaction in four age groups: university employees’ point of view

Contains fulltext : 79843.pdf (publisher's version ) (Closed access)PURPOSE: To investigate (a) differences in work characteristics and (b) determinants of job satisfaction among employees in different age groups. METHODS: A cross-sectional questionnaire was filled in by 1,112 university employees, classified into four age groups. (a) Work characteristics were analysed with ANOVA while adjusting for sex and job classification. (b) Job satisfaction was regressed against job demands and job resources adapted from the Job Demands-Resources model. Results : Statistically significant differences concerning work characteristics between age groups are present, but rather small. Regression analyses revealed that negative association of the job demands workload and conflicts at work with job satisfaction faded by adding job resources. Job resources were most correlated with more job satisfaction, especially more skill discretion and more relations with colleagues. CONCLUSIONS: Skill discretion and relations with colleagues are major determinants of job satisfaction. However, attention should also be given to conflicts at work, support from supervisor and opportunities for further education, because the mean scores of these work characteristics were disappointing in almost all age groups. The latter two characteristics were found to be associated significantly to job satisfaction in older workers

Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation

Specifying the number and locations of the translation vectors for wavelet neural networks (WNNs) is of paramount significance as the quality of approximation may be drastically reduced if initialization of WNNs parameters was not done judiciously. In this paper, an enhanced fuzzy C-means algorithm, specifically the modified point symmetry–based fuzzy C-means algorithm (MPSDFCM), was proposed, in order to determine the optimal initial locations for the translation vectors. The proposed neural network models were then employed in approximating five different nonlinear continuous functions. Assessment analysis showed that integration of the MPSDFCM in the learning phase of WNNs would lead to a significant improvement in WNNs prediction accuracy. Performance comparison with the approaches reported in the literature in approximating the same benchmark piecewise function verified the superiority of the proposed strategy