Factors associated with high nutrition risk by 10-year age group: Data from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BackgroundNutrition at midlife and beyond influences how an individual ages. Nutrition risk, the risk of poor nutritional health, is highly prevalent in community-dwelling adults in these age groups. As the factors associated with nutrition risk may vary between different age groups, research is needed on the differences in nutrition risk between age groups.AimTo examine the social, demographic, and health factors associated with high nutrition risk, determined using SCREEN-8, using data from the Canadian Longitudinal Study on Aging (CLSA), stratified by 10-year age groups.MethodsUsing the baseline and first follow-up waves of the CLSA, bivariate multivariable logistic regression was conducted to examine the variables associated with high nutrition risk (SCREEN-8 score < 38) by 10-year age group.ResultsHigher levels of social support, higher social standing, more frequent participation in community activities, screening negative for depression, and higher levels of self-rated general health, healthy aging, and oral health were consistently associated with lower odds of being at high nutrition risk across all age groups at both baseline and follow-up.ConclusionIndividuals with low levels of social support, low social standing, infrequent participation in community activities, poor general health, poor healthy aging, poor oral health, or who screen positive for depression should be screened proactively for nutrition risk. Programs and policies designed to address social support, social standing, participation in community activities, depression, health, healthy aging, and oral health may also help reduce the prevalence of high nutrition risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".