<i>Dietary Intake of Older Adults</i>in the Kingston Area
Bibliographic record
Abstract
The objectives of this research were to describe the dietary intake and identify risk factors for poor dietary intake in community-dwelling older adults living in the Kingston, Frontenac, and Lennox & Addington Health Unit area. Dietary intake information was collected from a convenience sample of 105 relatively healthy, active older adults (84 women, 21 men) using 24-hour recalls from three non-consecutive days. Risk factors for poor dietary intake were identified through a structured interview. Multiple linear regression was used to generate a model to predict dietary intake, which was measured using a diet score based on Canada's Food Guide to Healthy Eating. Group averages reflected reasonable diet quality, but some subjects had very low nutrient intakes, particularly of zinc and vitamins B6, B12, and C. On average, women had a lower-than-recommended intake from all food groups, while men consumed adequate amounts of all food groups except milk products. Higher scores indicated better overall diet quality, and the following were significant predictors of a high diet score: "almost always" preparing one's own meals, food "almost always" or "sometimes/never" tasting good, eating lunch every day, and taking fewer prescription medications. This model requires validation with a larger and more diverse population of community-dwelling 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 | 0.000 | 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 | 0.000 | 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".