Psychosoziale Merkmale der Arbeit, Überforderungserleben und Depressivität

Die vorliegende Studie untersuchte die im Job-Demand-Control-Support-Modell und Effort-Reward-Imbalance-Modell beschriebenen Tätigkeitsmerkmale in Bezug auf Depressivität in einer Stichprobe von 265 Erwerbstätigen. Anhand konfirmatorischer Faktorenanalysen wurden Gemeinsamkeiten und Unterschiede beider Modelle geprüft. Anschließend wurde die Bedeutung der nachweisbaren Tätigkeitsmerkmale für die Vorhersage von Depressivität getestet und untersucht, inwieweit die Effekte durch Überforderungserleben mediiert werden. Die Analysen zeigten, dass die Modelle sowohl gemeinsame (Arbeitsintensität bzw. berufliche Anforderungen) als auch distinkte Arbeitsmerkmale (Tätigkeitsspielraum, Arbeitsplatzsicherheit, beruflicher Status, soziale Anerkennung) erfassen. Hohe Arbeitsintensität, geringe Arbeitsplatzsicherheit und fehlende soziale Anerkennung standen in signifikantem Zusammenhang mit Depressivität. Anders als erwartet war der berufliche Status positiv mit Depressivität assoziiert, während für den Tätigkeitsspielraum keine signifikanten Effekte nachweisbar waren. Das Pfadmodell bestätigte sowohl direkte als auch durch Überforderungserleben vermittelte Zusammenhänge zwischen den Tätigkeitsmerkmalen und Depressivität (39 % Varianzaufklärung). Die Ergebnisse bieten eine Grundlage für die Identifizierung potenzieller Risikofaktoren für das Auftreten depressiver Symptome am Arbeitsplatz. This study examined the job characteristics in the Job-Demand-Control-Support Model and in the Effort-Reward Imbalance Model with regard to depression in a sample of 265 employees. First, we tested by means of confirmatory factor analysis similarities and differences of the two models. Secondly, job characteristics were introduced as predictors in a path model to test their relation with depression. Furthermore, we examined whether the associations were mediated by the experience of excessive demands. Our analyses showed the demand/effort component to be one common factor, while decision latitude and reward (subdivided into the three facets of job security, social recognition, and status-related reward) remained distinctive components. Employees with high job demands/effort, low job security, low social recognition, but high status-related rewards reported higher depression scores. Unexpectedly, status-related rewards were positively associated with depression, while we found no significant effects for decision latitude. The path models confirmed direct as well as mediation effects (through experienced excessive demands) between job characteristics and depression (39 % explained variance in depression). Our results could be useful to identify possible job-related risk factors for depression.

Test of a mediation model of perceived organisational support

This study examined the mediating influence of trust in organization (TIO) and organization-based self-esteem (OBSE) on the relationship between perceived organization support (POS) and its work outcomes. Data were obtained from employee–supervisor dyads from multiple organizations located in a major city in southern China. Structural equation modeling results revealed that: (a) POS related to TIO and OBSE and (b) TIO and OBSE fully mediated the relationship between POS and the work outcomes of organizational commitment and in-role performance, but partially mediated the POS–organizational citizenship behavior relationship.

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool.

A co-cultured skin model based on cell support membranes

Tissue engineering of skin based on collagen:PCL biocomposites using a designed co-culture system is reported. The collagen:PCL biocomposites having collagen:PCL (w/w) ratios of 1:4, 1:8, and 1:20 have been proven to be biocompatible materials to support both adult normal human epidermal Keratinocyte (NHEK) and mouse 3T3 fibroblast growth in cell culture, respectively, by Dai, Coombes, et al. in 2004. Films of collagen:PCL biocomposites were prepared using non-crosslinking method by impregnation of lyophilized collagen mats with PCL/dichloromethane solutions followed by solvent evaporation. To mimic the dermal/epidermal structure of skin, the 1:20 collagen:PCL biocomposites were selected for a feasibility study of a designed co-culture technique that would subsequently be used for preparing fibroblast/biocomposite/keratinocyte skin models. A 55.3% increase in cell number was measured in the designed co-culture system when fibroblasts were seeded on both sides of a biocomposite film compared with cell culture on one surface of the biocomposite in the feasibility study. The co-culture of human keratinocytes and 3T3 fibroblasts on each side of the membrane was therefore studied using the same co-culture system by growing keratinocytes on the top surface of membrane for 3 days and 3T3 fibroblasts underneath the membrane for 6 days. Scanning electron microscopy (SEM) and immunohistochemistry assay revealed good cell attachment and proliferation of both human keratinocytes and 3T3 fibroblasts with these two types of cells isolated well on each side of the membrane. Using a modified co-culture technique, a co-cultured skin model presenting a confluent epidermal sheet on one side of the biocomposite film and fibroblasts populated on the other side of the film was developed successfully in co-culture system for 28 days under investigations by SEM and immunohistochemistry assay. Thus, the design of a co-culture system based on 1:20 (w/w) collagen:PCL biocomposite membranes for preparation of a bi-layered skin model with differentiated epidermal sheet was proven in principle. The approach to skin modeling reported here may find application in tissue engineering and screening of new pharmaceuticals. © 2005 Elsevier Inc. All rights reserved.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.