Canadian Hospital Food Service Practices to Prevent Malnutrition
Bibliographic record
Abstract
Purpose: The study aimed to determine current practice, barriers, and enablers of foodservices in Canadian hospitals relative to guiding principles for best practice to prevent malnutrition. Methods: Foodservice managers completed a 55-item cross-sectional, online survey (closed- and open-ended questions). Results: Survey responses (n = 286) were from diverse hospitals in all Canadian regions; 56% acute care; 13% had foodservices contracted out; and 60% had a reporting structure combined with clinical nutrition. Predominantly, foodservice systems were 43% in-house versus 41% pre-prepared, 46% cook–serve food production, 64% meals assembled centrally (on-site), and 40% non-selective menus with limited opportunities for patient choice in advance or at meals. The “regular menu” (44%) was most commonly served as 3 meals, no snacks at specific times. Energy and protein-dense menus were available, but not widespread (9%). Daily energy targets ranged from 1200 to 2400 kcal and 32% of respondents viewed protein targets as important. The number of therapeutic diets varied from 2 to 150. Conclusions: Although hospital foodservice practices vary across Canada, the survey results demonstrate gaps in national evidence-based practices and an opportunity to formalize guiding principles. This work highlights the need for standards to improve practice through patient-centered, foodservice practices focused on addressing malnutrition.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".