<i>Dietary Vitamin D Intake</i> Among Elderly Residents in a Veterans’ Centre
Bibliographic record
Abstract
PURPOSE: Dietary vitamin D intake was assessed among residents in a long-term care (LTC) facility, so that recommendations could be made about vitamin D supplementation. METHODS: Three-day tray audits were completed for all meals and snacks, including nutritional supplements (Boost and/or high-protein pudding). Total daily and three-day vitamin D intake was calculated for each resident, and the total sample was compared with the recommended Adequate Intake (AI) of 600 IU. Vitamin D content was calculated using the Canadian Nutrient File and product labels. Resident charts were reviewed for micronutrient supplements and diagnoses. RESULTS: The daily average vitamin D available to and consumed by 30 residents was 414 IU and 295 IU, respectively. Those provided with nutritional supplements received an average of 480 IU and consumed 357 IU, while those without received an average of 245 IU and consumed 207 IU. Thirty-three percent of residents were diagnosed with osteoporosis, osteoarthritis, and falls and/or fractures. Vitamin D micronutrient supplementation varied from above 600 IU (43%) to below 600 IU (30%) to no supplementation (27%). CONCLUSIONS: None of the study participants met the recommended AI of 600 IU through dietary sources alone. Study results suggest that all LTC residents require vitamin D micronutrient supplementation of at least 400 IU to achieve the recommended AI of 600 IU.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".