Changes in executive function in the Canadian longitudinal study on aging over 3-years: A focus on social determinants of health
Bibliographic record
Abstract
Maintaining executive functions, including planning, inhibition, and decision-making skills, is important for autonomy and activities of daily living. There is a growing body of evidence linking social determinants and cognitive aging, but less is known about the potential role of social determinants in changes in executive functioning over time. Using data from the Canadian Longitudinal Study on Aging (CLSA), a large cohort of mid-aged and older adults, we examined changes in executive function over a 3-year period. Specifically, we focused on the role of social determinants (i.e., social positioning, social support, education) in explaining these changes. Executive function was measured at baseline and follow-up 3 years later using the Mental Alteration Test (MAT). We computed a reliable change index (RCI) and used a multiple linear regression model to examine the associations between known correlates and change in executive function over the 3-year period (n = 29,344). Older age, higher household income, and greater educational attainment predicted declines in executive function. Health factors (e.g., depression symptoms, physical activity levels) and many social determinants (sexual orientation, gender identity, race, and perceived social standing) were not associated with change in executive function. These results suggest that social determinants of health may be related to initial differences in cognitive functioning (i.e., cross-sectional differences) rather than more rapid cognitive aging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".