Determining Patient Satisfaction, Nutrition, and Environmental Impacts of Inpatient Food at a Tertiary Care Hospital in Canada: A Prospective Cohort Study
Bibliographic record
Abstract
While hospital meals are designed to meet the nutritional requirements associated with illness or surgery, competing priorities often take precedence over food quality, contributing to poor patient satisfaction, in-hospital malnutrition, and high food waste. The environmental impacts of hospital food services are a less well-characterized dimension of this complex problem. A prospective cohort study of patients admitted for select abdominal surgeries between June and October 2021 was conducted at a tertiary care hospital in Canada. Greenhouse gas emissions and land-use impacts associated with all food items served were estimated, and patient food waste was weighed for each meal. Patients’ experience of hospital food was measured at discharge. Nutrition was assessed by comparing measured oral intake to minimum caloric and protein requirements. On average, food served in hospital resulted in 3.75 kg CO2e/patient/day and 6.44 m2/patient/day. Average food waste was 0.88–1.39 kg/patient/day (37.5–58.9% of food served). Patients met their caloric and protein requirements on 9.8% and 14.8% of days in hospital, respectively. For patient satisfaction, 75% of overall scores were lower than the industry benchmark, and food quality scores were inversely correlated with quantities of food wasted. Redesigning inpatient food offerings to feature high-quality, low-emissions meals could lessen their environmental impacts while improving patient nutritional status and experience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".