The Hospital Food Experience Questionnaire Predicts Adult Patient Food Intake
Bibliographic record
Abstract
Purpose: Describe food/beverage intake among all patients and those with low meal intake and determine if the Hospital Food Experience Questionnaire (HFEQ), or its shorter version (HFEQ-sv), predicts food intake while considering patient (e.g., gender) and hospital characteristics (e.g., foodservice model). Methods: Cross-sectional study of 1087 adult patients from 16 hospitals in Ontario, Canada. The valid and reliable HFEQ assessed patients’ meal quality perceptions. Visual estimation determined overall meal and food/beverage intake using the Comstock method. Binary logistic regressions tested the association between patient and hospital characteristics and whether HFEQ or HFEQ-sv scores added utility in predicting overall meal intake (≤50% vs. ≥75%). Results: Approximately 29% of patients consumed ≤50% of their meal. Models assessing patient and hospital characteristics and either the HFEQ or the HFEQ-sv were significant (LRT(43) = 72.25, P = 0.003; LRT(43) = 93.46, P < 0.001). Men and higher HFEQ or HFEQ-sv scores demonstrated significantly higher odds of ≥75% meal consumption. Considering HFEQ or HFEQ-sv scores explained greater variance in meal intake and resulted in better model fits. Conclusions: The HFEQ and HFEQ-sv predict patient meal intake when adjusting for covariates and add utility in understanding meal intake. Either version can be confidently used to support menu planning and food delivery to promote food intake.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".