Factors associated with high nutrition risk by 10-year age group: Data from the Canadian Longitudinal Study on Aging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it