Health and minority stress in explaining change in executive function: Results from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease is characterized by deficits in multiple cognitive domains, one being executive function (e.g., planning, inhibition, decision making). Maintaining executive functions is important for autonomy and independence in daily living. Minority stress is linked to cross‐sectional differences in executive function, but less is known about the potential role of minority stress in changes in executive functioning over time. Method 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 health (e.g., medical conditions, modifiable health behaviors) and minority stress factors (sexual orientation, gender identity, race, and perceived social standing) in explaining these changes. Demographic, health, and minority stress variables were collected at baseline. The Mental Alteration Test (MAT), a cognitive switching task, was administered to measure executive function at baseline (2011‐2015) and follow‐up (2015‐2018). Using the MAT, we computed a reliable change index, accounting for practice effects and variability in scores at follow‐up. Negative scores reflect a more reliable decline in executive function. We used multivariable linear regression and the analytic sample was n = 27,714. Result Older age, higher household income, and greater educational attainment predicted reliable declines in executive function. There were no sex differences. Surprisingly, there were also no health factors (e.g., depression symptoms, physical activity levels) or minority stress variables (sexual orientation, gender identity, race, and perceived social standing) that predicted change in executive function. Conclusion Although previous cross‐sectional research has established that older minoritized individuals tend to have lower cognitive performance compared to majority peers, minoritized identities do not appear to explain changes in executive function over a short follow‐up period. Additional longitudinal data is needed to understand better the long‐term impact of minority stress on 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".