Bibliographic record
Abstract
OBJECTIVES: At any age, good nutrition is important for maintaining good health. Seniors are at risk of declining nutritional status due to the physiological, psychological, economic and social changes that accompany aging. We investigated medical, psychological, social and environmental characteristics as both correlates and predictors of elevated nutritional risk in community-dwelling seniors. METHODS: Data came from a prospective study of 839 seniors aged 75 and over, in Montreal. Face-to-face interviews were conducted at baseline and at 12 months. The validated Elderly Nutrition Screening (ENS) tool was administered and subjects were assigned a level of "nutritional risk" based on the risk for energy and nutritional intake deficiencies. Using risk factors identified in the literature, analyses were performed to characterize those factors associated with both the level of risk at baseline and a change in risk over 12 months. RESULTS: At baseline, more than half (60%) of the participants were at elevated nutritional risk. Cross-sectional analyses supported the findings of previous research examining correlates of elevated nutritional risk. Longitudinal results showed that among those at low nutritional risk, only poor self-rated health was found to be a statistically significant predictor of elevated risk at 12 months (OR = 3.30, p < 0.05). CONCLUSION: Proper nutrition can promote healthy aging by preventing disease and disability, improving health outcomes and maintaining autonomy, resulting in decreased health care utilization and costs. The findings of this research highlight the need for longitudinal studies in order to better understand and target nutritional risk in community-dwelling seniors.
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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 | 0.002 |
| 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 | 0.001 | 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".