5,297 research outputs found
Plate versus bulk trolley food service in a hospital: comparison of patients’ satisfaction
Objective
The aim of this research was to compare plate with bulk trolley food service in hospitals in terms of patient satisfaction. Key factors distinguishing satisfaction with each system would also be identified.
Methods
A consumer opinion card (n = 180), concentrating on the quality indicators of core foods, was used to measure patient satisfaction and compare two systems of delivery, plate and trolley. Binary logistic regression analysis was used to build a model that would predict food service style on the basis of the food attributes measured. Further investigation used multinomial logistic regression to predict opinion for the assessment of each food attribute within food service style.
Results
Results showed that the bulk trolley method of food distribution enables all foods to have a more acceptable texture, and for some foods (potato, P = 0.007; poached fish, P = 0.001; and minced beef, P ≤ 0.0005) temperature, and for other foods (broccoli, P ≤ 0.0005; carrots, P ≤ 0.0005; and poached fish, P = 0.001) flavor, than the plate system of delivery, where flavor is associated with bad opinion or dissatisfaction. A model was built indicating patient satisfaction with the two service systems.
Conclusion
This research confirms that patient satisfaction is enhanced by choice at the point of consumption (trolley system); however, portion size was not the controlling dimension. Temperature and texture were the most important attributes that measure patient satisfaction with food, thus defining the focus for hospital food service managers. To date, a model predicting patient satisfaction with the quality of food as served has not been proposed, and as such this work adds to the body of knowledge in this field. This report brings new information about the service style of dishes for improving the quality of food and thus enhancing patient satisfaction
Visual7W: Grounded Question Answering in Images
We have seen great progress in basic perceptual tasks such as object
recognition and detection. However, AI models still fail to match humans in
high-level vision tasks due to the lack of capacities for deeper reasoning.
Recently the new task of visual question answering (QA) has been proposed to
evaluate a model's capacity for deep image understanding. Previous works have
established a loose, global association between QA sentences and images.
However, many questions and answers, in practice, relate to local regions in
the images. We establish a semantic link between textual descriptions and image
regions by object-level grounding. It enables a new type of QA with visual
answers, in addition to textual answers used in previous work. We study the
visual QA tasks in a grounded setting with a large collection of 7W
multiple-choice QA pairs. Furthermore, we evaluate human performance and
several baseline models on the QA tasks. Finally, we propose a novel LSTM model
with spatial attention to tackle the 7W QA tasks.Comment: CVPR 201
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Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.
The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24
Contingent Valuation of Consumers’ Willingness-to-Pay for Organic Food in Argentina
Throughout these last years, organic agriculture has undergone a remarkable expansion due, among other things, to the greater interest shown by consumers aware of food safety concerns involving real or perceived quality risks [1]. This paper aims to estimate consumers’ willingness to pay (WTP) for organic food products available in the Argentinean domestic market, with a view to providing some useful insights to gain support and outline strategies for promotion of organic production, marketing, regulation, and labelling programs of organic food products. A Binomial Multiple Logistic Regression model is estimated with data from a food consumption survey conducted in Buenos Aires city, Argentina, in April 2005. The Contingent Valuation Method was chosen in order to calculate their WTP for five organic selected products: Regular Milk, Leafy Vegetables, Whole Wheat Flour, Fresh Chicken and Aromatic Herbs. The empirical results reveal that consumers are willing to pay a premium for these products and that although prices play an important role, lack of store availability and of a reliable regulatory system to mitigate quality risks constraint consumption of organic products in this country.Willingness-to-pay, Food attributes, Organics, Demand and Price Analysis,
Small bowel stomas are associated with higher risk of circulating food-specific-IgG than patients with organic gastrointestinal conditions and colostomies
Objective The effects of food sensitivity can easily be masked by other digestive symptoms in ostomates and are unknown. We investigated food-specific- IgG presence in ostomates relative to participants affected by other digestive diseases.
Design Food-specific- IgG was evaluated for 198 participants with a panel of 109 foods. Immunocompetency status was also tested. Jejunostomates, ileostomates and colostomates were compared with individuals with digestive tract diseases with inflammatory components (periodontitis, eosinophilic esophagitis, duodenitis, ulcerative colitis, Crohn’s disease and appendicitis), as well as food malabsorption due to intolerance. A logistic regression model with covariates was used to estimate the effect of the experimental data and demographic characteristics on the likelihood of the immune response.
Results Jejunostomates and ileostomates had a significant risk of presenting circulating food-specific- IgG in contrast to colostomates (OR 12.70 (p=0.002), 6.19 (p=0.011) and 2.69 (p=0.22), respectively). Crohn’s disease, eosinophilic esophagitis and food malabsorption groups also showed significantly elevated risks (OR 4.67 (p=0.048), 8.16 (p=0.016) and 18.00 (p=0.003), respectively), but not the ulcerative colitis group (OR 2.05 (p=0.36)). Individuals with profoundly or significantly reduced, and mild to moderately reduced, levels of total IgG were protected from the formation of food-specific IgG (OR 0.09 (p=\u3c0.001) and 0.33 (p=0.005), respectively). Males were at higher risk than females.
Conclusion The strength of a subject’s immunocompetence plays a role in the intensity to which the humoral system responds via food-specific- IgG. An element of biogeography emerges in which the maintenance of a colonic space might influence the risk of having circulating food-specific- IgG in ostomates.
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