The association between functional social support, marital status and memory in middle-aged and older adults: An analysis of the canadian longitudinal study on aging
Bibliographic record
Abstract
PURPOSE: Although several studies have reported positive associations between functional social support (FSS) and memory, few have explored how other social variables, such as marital status, may affect the magnitude and direction of this association. We examined whether marital status modifies the association between FSS and memory in a sample of community-dwelling, middle-aged and older adults. METHODS: Data at three timepoints, spanning six years, were analyzed from the Tracking Cohort of the Canadian Longitudinal Study on Aging (n = 10,318). Linear mixed models were used to regress memory onto FSS across all three timepoints, adjusting for multiple covariates. The moderating effect of marital status was assessed by adding its interaction with FSS in the model. Separate regression models were built for overall FSS and four subtypes (positive interactions, affectionate, emotional/informational, and tangible support). RESULTS: We found significant and positive adjusted associations for overall FSS (β: 0.07; 95 % CI: 0.01, 0.13), positive interactions (β: 0.06; 95 % CI: 0.01, 0.11), and affectionate support (β: 0.05; 95 % CI: 0.00, 0.11) with memory. However, the interaction between marital status and FSS (overall and subtypes) was not statistically significant (likelihood ratio test p-value = 0.75), indicating that FSS does not have differing effects on memory depending on marital status. CONCLUSION: Our findings do not provide evidence to suggest that marital status affects the association between FSS and memory in middle-aged and older adults. Nonetheless, policymakers and practitioners should take a comprehensive approach when exploring how various dimensions of social relationships may uniquely influence cognitive trajectories.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".