ADEQUACY OF MICRONUTRIENT INTAKE IN LONG-TERM CARE RESIDENTS: MAKING THE MOST OF MEALTIMES (M3)
Bibliographic record
Abstract
Food intake of residents living in long term care (LTC) is known to be poor. Yet, our understanding of which micronutrients are inadequately consumed is limited. The M3 prevalence study included an assessment of food intake from 639 residents (mean 86.8 ± 7.8 yrs old) in 32 nursing homes from four provinces (AB, ON, NB, MB) in Canada. Researchers rigorously collected weighed (main plate items) and estimated (snacks, beverages, side dishes) food and beverage intake from three non-consecutive days. Dietary records were analyzed for energy and micronutrient intakes using the Food Processor Nutrition Analysis Software (ESHA, version 10.14.1) and usual intakes were estimated by adjustment for intra-individual variation on all participants. Micronutrient intakes were compared with their estimated average requirement (EAR), when available, using the EAR cut-point method or with their Adequate Intake (AI) when an EAR was not available. Energy intake was 1715 ± 291 kcal and 1481 ± 261 (mean ± SD) for males (n=197) and females (n=435), respectively. Nutrients where the prevalence of inadequacy was greater than 50% for males and females were: vitamin B6, D, E, folate, calcium, magnesium and zinc. More than 50% of participants were below the AI for vitamin K and Potassium. These results document a high prevalence (10 of 20 micronutrients assessed) of inadequate micronutrient intakes in residents living in LTC in Canada. Interventions to promote more nutrient dense foods in LTC menus are required. (Funded by Canadian Institutes of Health Research)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".