Current Practices and Priority Issues Regarding Nutritional Assessment and Patient Satisfaction with Hospital Menus
Bibliographic record
Abstract
PURPOSE: Patient satisfaction with hospital food enhances consumption and adequate intake of nutrients required for recovery from illness/injury and maintenance of health; accordingly, the nutrient content of the menu must balance patient preferences. This study of Ontario hospital foodservice departments collected data on current practices of analyzing the nutritional adequacy and assessing patient satisfaction with menus, and it explored perceptions of priority issues. METHODS: Foodservice managers/directors from 57 of 140 (41%) hospitals responded to cross-sectional in-depth telephone interviews. Deductive analysis of responses to open-ended questions supplemented quantitative data from closed-ended questions. RESULTS: The hospitals without long-term care facilities (LTCFs) assessed regular (58%), therapeutic (53%), and texture-modified (47%) menus for nutritional adequacy. This differed from hospitals governing LTCFs where there was a higher frequency of assessment of regular (75%), therapeutic (75%), and textured-modified (66%) menus. Most departments (86%-94%) obtained patient satisfaction feedback at the departmental/corporate levels. Many identified budget and labour issues as priorities rather than assessing menus for nutritional adequacy and patient satisfaction. CONCLUSIONS: Hospital menus were not consistently assessed for nutritional adequacy and patient satisfaction; common assessment methodologies and standards were absent. Compliance standards seem to increase the frequency of menu assessment as demonstrated by hospitals governing LTCFs.
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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 | 0.007 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".