Balancing Patients’ Eating Habits with Planetary Health—Pilot Study to Decrease Food Waste with Vegetarian Lunches using a Quality Improvement Approach
Bibliographic record
Abstract
Background Patient health is greatly impacted by increased likelihood of malnutrition if food is not consumed. Food waste also contributes to greenhouse gas emissions and may be possibly reduced by offering vegetarian food options. Therefore, a Plan-Do-Study-Act intervention of “Vegetarian Week” was conducted in an urban geriatric rehabilitation unit. Method Food waste was measured using the Visual Estimation Method, and the proportion of waste before and after the intervention was compared using a two-tailed z-test. Surveys assessed patients’ attitudes towards vegetarian meals. Results Study population was of 54 (2022) and 65 (2023) patients with the majority being male (62.5%), average age 74.5 years, average length of stay 33 days. Comparing pre- and post-intervention periods, overall food wastage increased at: breakfast (22% to 32%), lunch (22% to 32%), and dinner (20% to 25%) with p values <.001. Considering lunch entrées only, wastage increased from 17% to 38%, with vegetarian entrees wasted (46%) more than non-vegetarian ones (34%). Vegetarian patients wasted (37%) as much as non-vegetarians (39%). Survey response rate pre-PDSA was 45%, with most patients (76%) reporting eating an omnivorous diet, a prior awareness of personal and planetary health benefits of vegetarian diets (59%), and previously trying vegetarian dishes (62%). Post-PDSA survey response rate was lower (22%) with 57% not willing to try vegetarian dishes again. Conclusion Through evaluation of the patient food experience with Visual Estimation Method and surveys, the very complex issue of food satisfaction was explored in older adults. Although food waste was not decreased during this “Vegetarian Week” pilot, improving patient and planetary health requires ongoing efforts.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".