Putting quality food on the tray: Factors associated with patients’ perceptions of the hospital food experience
Bibliographic record
Abstract
INTRODUCTION: Perceptions of hospital meal quality can influence patient food intake. Understanding what patients prioritise and what they think of current meals can support menu development. The present study assessed patients' food and food-related priorities for hospital meals and their sensory experience using the Hospital Food Experience Questionnaire (HFEQ). Factors independently associated with the HFEQ were determined. METHODS: Cross-sectional study (n = 1087 patients; 16 Ontario hospitals). Patients completed the HFEQ at a single meal. Descriptive statistics determined the importance of food traits and ratings of a served meal using 22 HFEQ questions (five-point Likert scales, total score 110). Bivariate and multivariable linear regression tested the association between patient and hospital characteristics and HFEQ score. RESULTS: = 2.34, p < 0.001). Older and woman-identifying patients were more likely to have a higher score. Foodservice models were associated with HFEQ. Cold-plated rethermed food resulted in the lowest HFEQ. Local food use > 10% was associated with lower HFEQ score, whereas larger hospitals had a higher score. CONCLUSIONS: Patients prioritised taste, freshness and food that met their dietary needs. Meal sensory ratings were average. A gap exists between what patients want in hospital meals and what they receive. Attention to patient demographics and food delivery that retains sensory properties and supports choice may increase HFEQ score.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".