The adequacy of texture modified menus in long-term care facilities in Nova Scotia with relation to Canada's Food Guide
Bibliographic record
Abstract
Canada's Food Guide (CFG) has been the standard for food group recommendations since 1942 and is the foundation for many institutions' foodservice guidelines. While CFG remains the standard for long-term care facilities guidelines, the usefulness when combined with texture modified diet orders is unclear. The purpose of this study was to determine the adequacy of texture modified menu offerings in minced and pureed form in relation to CFG. Menus were collected from 34 facilities across Nova Scotia with 20 or more residents who offered texture modified diets and were reviewed using a tool developed by the principal investigator. The average offerings across facilities were analyzed using a non-parametric Sign test for both ground and pureed textures. All menu offerings across both textures and all facilities were significantly different than the recommendations in CFG, and fruits and vegetables and grains food groups were significantly below the recommendations. These results show that texture modified diets frequently fall short of meeting the CFG recommendations. This is of significant concern as the elderly population in Canada is increasing quickly. Texture modified diets are becoming increasingly common and focus should be driven towards improving their palatability, energy and protein content, and consistency as food and nutrition play a role in quality of life, especially in long-term care dwelling seniors. Further evaluation of consumption versus meal offerings and nutrient analysis of menu offerings would present more insight into the nutritional status of seniors in institutionalized care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".