Social factors associated with changes in nutrition risk scores measured using SCREEN-8: data from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Purpose: To examine the social network factors associated with changes in nutrition risk scores, measured by SCREEN-8, over three years, in community-dwelling Canadians aged 45 years and older, using data from the Canadian Longitudinal Study on Aging (CLSA). Methods: Change in SCREEN-8 scores between the baseline and first follow-up waves of the CLSA was calculated by subtracting SCREEN-8 scores at follow-up from baseline scores. Multivariable linear regression was used to examine the factors associated with change in SCREEN-8 score. Results: The mean SCREEN-8 score at baseline was 38.7 (SD = 6.4), and the mean SCREEN-8 score at follow-up was 37.9 (SD = 6.6). The mean change in SCREEN-8 score was −0.90 (SD = 5.99). Higher levels of social participation (participation in community activities) were associated with increases in SCREEN-8 scores between baseline and follow-up, three years later. Conclusions: Dietitians should be aware that individuals with low levels of social participation may be at risk for having their nutritional status decrease over time and consideration should be given to screening them proactively for nutrition risk. Dietitians can develop and support programs aimed at combining food with social participation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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".