Challenges in Planning Long-term Care Menus: That Meet Dietary Recommendations
Bibliographic record
Abstract
PURPOSE: Long-term care (LTC) homes plan menus based on Eating Well with Canada's Food Guide (CFG) recommendations for older adults. To determine whether recommended CFG servings and nutrients were being provided, we analyzed the menu of a large LTC facility in a metropolitan area and compared our analysis with a similar one conducted in 2000. METHODS: A full week's menu from a large Saskatoon LTC facility was analyzed and compared with CFG and recent Dietary Recommended Intake nutrient recommendations. The menu was analyzed using The Food Processor SQL. The 2011 menu was compared with the similar 2000 menu analysis to permit an evaluation of changes over a decade. RESULTS: The 2011 menu demonstrated a significant improvement in servings of vegetables and fruit (4.6 to 7.2 servings). Servings of grain products had declined from 4.9 to 3.6 and servings of milk and alternatives had declined from 2.4 to 1.2 since 2000. Servings of meat and alternatives, total carbohydrate, and protein were not significantly different. Foods on the 2011 menu were lower in fat and higher in dietary fibre and offered more vitamins and minerals. CONCLUSIONS: Greater attention to the planning of LTC menus may explain improvements in the 2011 LTC menu. The current menu, however, needs to overcome the challenges that prevent it from meeting CFG recommendations for older adults.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".