Alignment of menu items offered in Canadian long-term care homes with Canada’s food guide and the Diabetes Canada Clinical Practice Guidelines
Bibliographic record
Abstract
BACKGROUND: Many residents in long-term care (LTC) homes face the risk of malnutrition and non-communicable diseases like diabetes, underscoring the crucial role of menu planning. In most provinces, menu items offered in LTC homes must adhere to Canada's food guide (CFG). Other dietary guidelines, like those in Diabetes Canada Clinical Practice Guidelines (DCCP), provide recommendations for managing chronic disease; however, the alignment of individual menu items with CFG and DCCP is unknown. The objective was to assess the alignment of menu items offered in LTC homes with CFG and DCCP. METHODS: Using a four-week menu cycle designed for LTC, menu items (n = 1,365) were assessed using two nutrient profile models based on CFG and the DCCP. The Canadian Foods Scoring System (CFSS) categorized items as "very poor" to "excellent" choices according to CFG, and the DCCP nutrient profile model classified items as "least" to "mostly aligned" with DCCP. Descriptive statistics summarized menu items by CFSS and DCCP nutrient profile model categories across meal occasions and food categories. RESULTS: Overall, 52.8% of menu items served in LTC homes were rated "good" or "excellent" choices by CFSS, and 50.8% were classified as "most aligned" with the DCCP nutrient profile model. Afternoon Snacks had the highest proportion of the least healthy items. Legumes and Vegetables were the healthiest categories, and Sugars & Sweets, along with Combination Dishes, ranked as the least healthy. CONCLUSIONS: While about one-half of LTC menu items align with CFG and DCCP, opportunities remain to enhance their nutritional quality. Developing a translational tool based on nutrient profile models could simplify the application of food-based dietary guidelines, supporting more effective and aligned LTC menu planning.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".