Nutrition risk varies according to social network type: data from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: There were two primary objectives, namely: (1) to determine the social network types that Canadian adults aged 45 and older belong to and (2) to discover if social network type is associated with nutrition risk scores and the prevalence of high nutrition risk. DESIGN: A retrospective cross-sectional study. SETTING: Data from the Canadian Longitudinal Study on Aging (CLSA). PARTICIPANTS: 17 051 Canadians aged 45 years and older with data from baseline and first follow-up of the CLSA. RESULTS: CLSA participants could be classified into one of seven different social network types that varied from restricted to diverse. We found a statistically significant association between social network type and nutrition risk scores and percentage of individuals at high nutrition risk at both time points. Individuals with restricted social networks had lower nutrition risk scores and are more likely to be at nutrition risk, whereas individuals with diverse social networks had higher nutrition risk scores and are less likely to be at nutrition risk. CONCLUSIONS: Social network type was associated with nutrition risk in this representative sample of Canadian middle-aged and older adults. Providing adults with opportunities to deepen and diversify their social networks may decrease the prevalence of nutrition risk. Individuals with more restricted networks should be proactively screened for 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".