Examining Psychosocial Factors Influencing Nutrition Risk in Middle-Aged and Older Adults: Findings from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Our objective was to identify the psychosocial factors correlated with the development of high nutrition risk, as assessed by SCREEN-8, among Canadian adults categorized by ten-year age groups (45-54, 55-64, 65-74, and 75+). We utilized data from 17,051 partic-ipants in the tracking cohort of the Canadian Longitudinal Study on Aging and employed multivariable binomial logistic regression to identify the social and demographic factors associated with the emergence of high nutrition risk at the follow-up, three years sub-sequent to the baseline. Baseline data were gathered between 2011 and 2015. At baseline, 34.4% of participants across all age groups were at high nutrition risk, and 40.0% were at high risk at follow-up. Factors consistently associated with the development of high nutrition risk across all age groups included lower levels of social support, lower self-rated social standing, infrequent participation in sports or physical activities, infrequent par-ticipation in cultural or educational activities, and lower household incomes. Programs and policies addressing these factors may reduce the prevalence of high nutrition risk and the development 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".