Environmentally Friendly Health Care Food Services: A Survey of Beliefs, Behaviours, and Attitudes
Bibliographic record
Abstract
PURPOSE: There is increasing global interest in sustainability and the environment. A hospital/health care food service facility consumes large amounts of resources; therefore, efficiencies in operation can address sustainability. Beliefs, attitudes, and behaviours about environmentally friendly practices in hospital/health care food services were explored in this study. METHODS: Questionnaires addressed environmentally friendly initiatives in building and equipment, waste management, food, and non-food procurement issues. The 68 participants included hospital food service managers, clinical dietitians, dietary aides, food technicians, and senior management. Data analysis included correlation analysis and descriptive statistics. RESULTS: Average scores for beliefs were high in building and equipment (90%), waste management (94%), and non-food procurement (87%), and lower in food-related initiatives (61%) such as buying locally, buying organic foods, buying sustainable fish products, and reducing animal proteins. Average positive scores for behaviours were positively correlated with beliefs (waste management, p=0.001; food, p=0.000; non-food procurement, p=0.002). Average positive scores for attitude in terms of implementing the initiatives in health care were 74% for building and equipment, 81% for waste management, 70% for non-food procurement, and 36% for food. CONCLUSIONS: The difference in food-related beliefs, behaviours, and attitudes suggests the need for education on environmental impacts of food choices. Research is recommended to determine facilitators and barriers to the implementation of green strategies in health care. As food experts, dietitians can lead changes in education, practice, and policy development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".