Evaluating Menu Quality in Canadian Long-Term Care Homes: Navigating Compliance with Nutrient and Food-Based Standards
Bibliographic record
Abstract
Residents in Canadian long-term care (LTC) homes are nutritionally vulnerable due to advanced age, frailty, chronic conditions, and limited food intake. Ensuring menus meet both nutrient- and food-based requirements is challenging, as few practical tools exist to support compliance with the Dietary Reference Intakes and Canada’s food guide 2019. This thesis evaluated the healthfulness of individual menu items offered in LTC and assessed menu compliance with nutrient- and food-based standards to characterize compliant menus that can inform menu planning. Study 1 analyzed a four-week LTC menu using the Canadian Food Scoring System (CFSS) and Diabetes Canada Clinical Practice Guidelines (DCCP) nutrient profile models. Just over half of the items were rated “Good & Excellent” (52.8%, CFSS) or “Most aligned” (50.8%, DCCP). Study 2 identified major gaps in menu compliance and developed two practical tools to strengthen menu planning. These findings help identify gaps, guide substitutions, and support LTC in meeting standards to improve menu quality and resident health.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".