Social support and cognitive function in middle- and older-aged adults: descriptive analysis of CLSA tracking data
Bibliographic record
Abstract
BACKGROUND: Cognitive function is important for healthy aging. Social support availability (SSA) may modify cognitive function. We descriptively examined the association between SSA and cognitive function in a population-level sample of middle- and older-aged adults. METHODS: We analyzed the tracking dataset of the Canadian Longitudinal Study on Aging. Participants aged between 45 and 85 years answered questions about SSA and performed three cognitive tests (Rey Auditory Verbal Learning Test, Animal Fluency Test and Mental Alternation Test) via telephone. We divided global SSA and global cognitive function scores into tertiles and generated contingency tables for comparisons across strata defined by sex, age group, region of residence, urban vs. rural residence and education. RESULTS: The proportion of participants with low global cognitive function was often greater among persons who reported low global SSA. The proportion of persons with high cognitive function was greater in participants with high SSA. The findings were most pronounced for females, 45- to 54-year olds, all regions (especially Québec) except Atlantic Canada, urban dwellers and persons with less than high school education. CONCLUSIONS: Our results can help public health officials focus on providing social supports to subgroups of the population who would benefit the most from policy interventions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".