The present study was designed to investigate the causal relationships between work‐home interference (WHI) and one of its strongest correlates, quantitative workload. Two‐phase longitudinal data (with a 1‐year time lag) were gathered from 828 Dutch police officers. Drawing on the effort‐recovery model, and in line with the current WHI literature, we hypothesized that relatively high workload precedes increased levels of WHI 1 year later (‘normal’ causation). In addition, we tested the alternative hypothesis that relatively high levels of WHI predict increased workload 1 year later (‘reversed’ causation). Results of hierarchical regression analyses showed that workload and WHI have causal and reversed causal relationships across time, supporting both hypotheses (although effect sizes are limited). Accordingly, these findings suggest that workload is not merely an antecedent of WHI but is also a potential consequence. Copyright © 2007 John Wiley & Sons, Ltd.

No abstract

Resultaten De respondenten, van wie een groot deel een chronische aandoening rapporteerde (83%), hadden tijdens het verzuim contact met verschillende artsen: -89% had minstens 1 keer contact met de huisarts, 65% vaker.-92% had minstens 1 keer contact met de bedrijfsarts, 80% vaker. Terwijl 29% van de respondenten de medisch specialist als de feitelijk belangrijkste behandelaar tijdens verzuim zag, tegen 17% de huisarts en 11% de bedrijfsarts, zou 36% eigenlijk het liefst met de huisarts en 34% het liefst met de bedrijfsarts te maken hebben. Slechts 12% prefereerde de medisch specialist. Belangrijk vinden zij daarbij onafhankelijkheid, communicatie, kennis over de relatie tussen werk en gezondheid, en vooral vertrouwen. Patiënten beoordelen huisartsen op deze punten positiever dan bedrijfsartsen en zouden bij gezondheidsproblemen het liefst naar de huisarts gaan. Zij hebben geen probleem met intercollegiaal contact of met inzage in hun (elektronisch) patiëntendos-sier, mits ze hun informed consent kunnen geven. Conclusie Werknemers verwachten bij ziekteverzuim een prominentere rol van hun bedrijfsarts, maar zeker ook van hun huisarts.

No abstract

Abstract. The use of radar for precipitation measurement in mountainous regions is complicated by many factors, especially beam shielding by terrain features, which, for example, reduces the visibility of the shallow precipitation systems during the cold season. When extrapolating the radar measurements aloft for quantitative precipitation estimation (QPE) at the ground, these must be corrected for the vertical change of the radar echo caused by the growth and transformation of precipitation. Building on the availability of polarimetric data and a hydrometeor classification algorithm, this work explores the potential of machine learning methods to study the vertical structure of precipitation in Switzerland and to propose a more localised vertical profile correction. It first establishes the ground work for the use of machine learning methods in this context: from volumetric data of 30 precipitation events, vertical cones with 500 m vertical resolution are extracted. It is shown that these cones can well represent the vertical structure of different types of precipitation events (stratiform, convective, snowfall). The reflectivity data and the hydrometeor proportions from the extracted cones constitute the input for the training of artificial neural networks (ANNs), which are used to predict the vertical change in reflectivity. Lower height levels are gradually removed in order to test the ANN's ability to extrapolate the radar measurements to the ground level. It is found that ANN models using the information on hydrometeor proportions can predict from altitudes between 500 and 1000 m higher than the ANN based on only reflectivity data. In comparison to more traditional vertical profile correction techniques, the ANNs show less prediction errors made from all height levels up to 4000 m a.s.l., above which the ANNs lose predictive skill and the performance levels off to a constant value.