Evaluating Patient Experience with Food in a Hospital-Wide Survey
Bibliographic record
Abstract
Purpose: Patient dissatisfaction with hospital food is an important driver of poor food intake in hospitals. The objective of this study was to examine patient satisfaction with current menu offerings and explore patient preferences and values, in order to inform a patient-centred menu redesign. Methods: Between July and September 2021, a cross-sectional survey was distributed to inpatients receiving a lunch tray at Vancouver General Hospital, a large tertiary care centre in Vancouver, Canada. The survey was based on the Acute Care Hospital Foodservice Patient Satisfaction Questionnaire, with additional questions on food experience, factors impacting preferences for hospital meals, interest in plant-rich diets, and demographics. Results: The response rate was 5.5%, with 271 patients completing at least part of the survey. On a 5-point Likert scale, (5 – highest score; 1 – lowest score) satisfaction with food quality (mean = 3.09, p < 0.001) and the overall experience (mean = 3.54, p < 0.001) was lower than industry benchmark of 4, and qualitative feedback was generally negative. Open-ended responses indicated patients were interested in expanded cultural diversity in food provision, more fresh produce and better flavours, and were generally open to trying plant-rich foods. Conclusions: A number of opportunities for improvement were identified in this survey, which will inform an upcoming menu redesign in this institution.
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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 | 0.003 | 0.012 |
| 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 | 0.000 | 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".