Planning Micronutrient-Dense Menus in Ontario Long-Term Care Homes: Strategies and Challenges
Bibliographic record
Abstract
Prevalence of micronutrient malnutrition is high in individuals living in long-term care (LTC) homes with many individuals consuming low levels of vitamins B6, D, and E; folate; calcium; magnesium; and zinc. The focus of this research was to identify strategies and challenges encountered during development of micronutrient-dense menus for use in Ontario LTC homes and to examine costs associated with development of a menu with acceptable micronutrients. Semi-structured open-ended interviews were conducted with 13 menu planners (7 dietitians, 6 nutrition managers) in diverse LTC homes in Ontario. Data were thematically analyzed. A 7-day hypothetical menu meeting all nutrient requirements was developed and costed. Analysis of the interview data showed that menus are planned according to the Canada's Food Guide (2007) and focus placed on Dietary Reference Intakes of protein, fibre, calcium, and sodium. Little focus is placed on micronutrients. Flexibility in foods offered was important to accommodate the small volume of food consumed. Resident preferences were balanced against nutritional requirements. Challenges included planning for diverse populations, managing portion sizes, and balancing the budget. A hypothetical menu planned to contain adequate levels of all micronutrients is 49% higher in food costs than the amount currently provided to Ontario LTC homes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".