Ownership type and team climate in elderly care facilities: the moderating effect of stress factors

Aims. This paper is a report of a study examining the association between ownership type and perceived team climate among older people care staff. In addition, we examined whether work stress factors (time pressure, resident-related stress, role conflicts and role ambiguity) mediated or moderated the above mentioned association. Background. There has been a trend towards contracting out in older people care facilities in Finland and the number of private for-profit firms has increased. Studies suggest that there may be differences in employee well-being and quality of care according to the ownership type of older people care. Methods. Cross-sectional survey data was collected during the autumn of 2007 from 1084 Finnish female older people care staff aged 1869 years were used. Team Climate Inventory was used to measure team climate. Ownership type was divided into four categories: for-profit sheltered homes, not-for-profit sheltered homes, public sheltered homes and not-for-profit nursing homes. Analyses of covariance were used to examine the associations. Results. Team climate dimensions participative safety, vision and support for innovation were higher in not-for-profit organizations (both sheltered homes and nursing homes) compared to for-profit sheltered homes and public sheltered homes. Stress factors did not account for these associations but acted as moderators in a way that in terms of task orientation and participative safety employees working in for-profit organizations seemed to be slightly more sensitive to work-related stress than others. Conclusion. Our results suggest that for-profit organizations and public organizations may have difficulties in maintaining their team climate. In consequence, these organizations should focus more effort on improving their team climate.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Optimizing the balance between false positive and false negative error probabilities of confirmatory methods for the detection of veterinary drug residues

GC-MS data on veterinary drug residues in bovine urine are used for controlling the illegal practice of fattening cattle. According to current detection criteria, peak patterns of preferably four ions should agree within 10 or 20% from a corresponding standard pattern. These criteria are rigid, rather arbitrary and do not match daily practice. A new model, based on multivariate modeling of log peak abundance ratios, provides a theoretical basis for the identification of analytes and optimizes the balance between the avoidance of false positives and false negatives. The performance of the model is demonstrated on data provided by five laboratories, each supplying GC-MS measurements on the detection of clenbuterol, dienestrol and 19 beta-nortestosterone in urine. The proposed model shows a better performance than confirmation by using the current criteria and provides a statistical basis for inspection criteria in terms of error probabilities.

New FMO Type to Flag ROI in H264/AVC

This paper presents a new type of Flexible Macroblock Ordering (FMO) type for the H.264 Advanced Video Coding (AVC) standard, which can more efficiently flag the position and shape of regions of interest (ROIs) in each frame. In H.264/AVC, 7 types of FMO have been defined, all of which are designed for error resilience. Most previous work related to ROI processing has adopted Type-2 (foreground & background), or Type-6 (explicit), to flag the position and shape of the ROI. However, only rectangular shapes are allowed in Type-2 and for non-rectangular shapes, the non-ROI macroblocks may be wrongly flagged as being within the ROI, which could seriously affect subsequent processing of the ROI. In Type-6, each macroblock in a frame uses fixed-length bits to indicate to its slice group. In general, each ROI is assigned to one slice group identity. Although this FMO type can more accurately flag the position and shape of the ROI, it incurs a significant bitrate overhead. The proposed new FMO type uses the smallest rectangle that covers the ROI to indicate its position and a spiral binary mask is employed within the rectangle to indicate the shape of the ROI. This technique can accurately flag the ROI and provide significantly savings in the bitrate overhead. Compared with Type-6, an 80% to 90% reduction in the bitrate overhead can be obtained while achieving the same accuracy.

Outcomes and projected impact on vision restoration of the China million cataract surgeries program

Purpose: The recently completed Chinese "Million Cataract Surgeries Program" (MCSP) is among the largest such campaigns ever, providing 1.05 million operations. We report MCSP outcomes for the first time, in Jiangxi, the province with the greatest program output. Methods: Ten county hospitals participating in MCSP were selected in Jiangxi (range of gross domestic product per capita US$743-2998). Each hospital sought to enroll 75 consecutive MCSP patients aged ≥50 years. Data recorded included type of cataract procedure, bilateral uncorrected visual acuity (UCVA) and best-corrected visual acuity (BCVA), and refractive error pre- and ≥50 days postoperatively. Results: Among 715 patients (mean age 72.3±9.1 years, 55.5% female), preoperative UCVA was <3/60 (legally blind) bilaterally in 13.3% and unilaterally in the operated eye in 50.9%. No subjects had UCVA >6/18 preoperatively. Small incision cataract surgery was performed in 92.3% patients. Among 662 patients (92.6%) completing follow-up was ≥ 40 days after surgery, BCVA was ≥6/18 in 80.1%, UCVA was ≥6/18 in 57.1% and UCVA was <3/60 in 2.1%. Older age (p<0.001), female sex (p=0.04), worse refractive error (p=0.02) and presence of intra- (p=0.002) and postoperative surgical complications (p<0.001), were independently associated with worse postoperative UCVA. Based on these results, the MCSP cured an estimated 124,950 cases (13.3%×[100-2.1%]×1.05 million) of bilateral and 502,500 (50.9%×[100-2.1%]×1.05 million) of unilateral blindness. Conclusions: Due to relatively good outcomes and the large number of surgeries performed on blind persons, the sight-restoring impact of the MCSP was probably substantial. © Informa Healthcare USA, Inc.