Green Healthcare: You Are What You Serve: Healthy and Environmentally Friendly Food Service
Bibliographic record
Abstract
The quality of hospital food has long been the butt of comedians' jokes.More recently, hospitals have also been criticized for serving fast food.One recent U.S. survey, for example, found that 38% of top U.S. hospitals -six of the 16 "Honor Roll"hospitals listed by US News & World Report's 2001 ranking of "America's Best Hospitals"-have fast-food franchises on site ( ).Gottlieb and Shaffer found that more than 25% of 47 U.S. children's hospitals had fast-food franchises within them ( ).Meanwhile, a 1997 report from Toronto's Food Policy Council entitled "If the Health Care System Believed You Are What You Eat,"suggested that we need to transform hospital food service systems into facilities providing healthy food choices and local food ( ).In response to these criticisms, as well as out of a genuine concern for the welfare of their patients, a growing number of hospitals have started to focus more on the healthfulness of the food they serve.For example, Planetree hospitals, which are committed to creating healing environments for their patients and healthy workplaces for their staff (www.Planetree.org)pay particular attention not only to the quality of the food they serve but the nurturing role of food "as a source of pleasure, comfort and familiarity"during a stressful period of hospitalization.Many Planetree hospitals, for example, have small kitchens on each floor where family members can cook favourite foods for their loved ones and nutritionists can demonstrate healthy food preparation, while volunteers fill the halls with the smell of fresh baked goods every morning.In the U.K., two Scottish hospitals recently won the Healthy Choices Award from Scotland's Health Education Board, while in Wales a hospital in Powys, working with the Soil Association, now provides organic milk for its patients in spite of the difficulties imposed by World Trade Organization regulations that prevent organizations from specifying local produce.This latter example begins to show the links between healthy food and food that is produced in an environmentally sustainable manner -and the challenges involved in being environmentally and socially responsible!Given the growing concern with the potential health impacts of pesticide residues, particularly for children, and the fact that as a result of eco-toxicity and the contamination of food chains, we get 75 to 90% of our daily dose of persistent organic pollutants
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".