Hurdles at work: perceptions of hospital food handlers

Abstract Background Food handlers have a very important role in preventing food contamination during its preparation and distribution. This responsibility is even greater in hospitals, since a large number of patients have low immunity and consequently food contamination by pathogenic bacteria could be particularly harmful. Therefore, a good working environment and periodic training should be provided to food handlers by upper management. Methods This study is qualitative research by means of focus group and thematic content analysis methodologies to examine, in detail, the statements by food handlers working in the milk and specific-diet kitchens in a hospital to understand the problems they face in the workplace. Results We found that food handlers are aware of the role they play in restoring patients' health; they consider it important to offer a good-quality diet. However, according to their perceptions, a number of difficulties prevent them from reaching this aim. These include: upper management not prioritizing human and material resources to the dietetic services when making resource allocation decisions; a perception that upper management considers their work to be of lesser importance; delayed overtime payments; lack of periodic training; managers lacking administrative skills; insufficient dietitian staff assistants, leading to overwork, at the same time as there is an excess of dietitians; unhealthy environmental working conditions – high temperature, high humidity, loud and constant noise level, poor ventilation; lack of food, and kitchen utensils and equipment; and relationship conflicts with chief dieticians and co-workers. Conclusion From these findings, improvement in staff motivation could be achieved by considering non-financial incentives, such as improvement in working conditions and showing appreciation and respect through supervision, training and performance appraisal. Management action, such as investments in intermediary management so that managers have the capacity to provide supportive supervision, as well as better use of performance appraisal and access to training, may help overcome the identified problems.

Characterization of Staphylococcus aureus isolates in milk and the milking environment from small-scale dairy farms of Sao Paulo, Brazil, using pulsed-field gel electrophoresis

This research aimed to evaluate the occurrence of Staphylococcus aureus isolates in milk and in the milking environment of 10 small-scale farms (<400 L/d) located in the regions of Franca and Ribeirao Preto, state of Sao Paulo, Brazil. Two-hundred twenty samples of milk were collected from individual cows, along with 120 samples from bulk tank milk, 389 samples from milking equipment and utensils (teat cups, buckets, and sieves), and 120 samples from milkers' hands. Fifty-six Staph. aureus strains were isolated from 849 analyzed samples (6.6%): 12 (5.5%) from milk samples of individual cows, 26 (21.7%) from samples of bulk tank milk, 14 (3.6%) from samples collected from equipment and utensils, and 4 (3.3%) from samples from milkers' hands. Pulsed-field gel electrophoresis typing of the 56 Staph. aureus isolates by SmaI restriction enzyme resulted in 31 profiles (pulsotypes) arranged in 12 major clusters. Results of this study indicate a low incidence, but wide distribution of Staph. aureus strains isolated from raw milk collected from individual cows and surfaces of milkers' hands and milking equipment in the small-scale dairy farms evaluated. However, the high percentage of bulk milk samples found with Staph. aureus is of public health concern because raw, unprocessed milk is regularly consumed by the Brazilian population.

THE SLOAN DIGITAL SKY SURVEY CO-ADD: A GALAXY PHOTOMETRIC REDSHIFT CATALOG

We present and describe a catalog of galaxy photometric redshifts (photo-z) for the Sloan Digital Sky Survey (SDSS) Co-add Data. We use the artificial neural network (ANN) technique to calculate the photo-z and the nearest neighbor error method to estimate photo-z errors for similar to 13 million objects classified as galaxies in the co-add with r < 24.5. The photo-z and photo-z error estimators are trained and validated on a sample of similar to 83,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey, the Deep Extragalactic Evolutionary Probe Data Release 3, the VIsible imaging Multi-Object Spectrograph-Very Large Telescope Deep Survey, and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than sigma(68) = 0.031. After presenting our results and quality tests, we provide a short guide for users accessing the public data.