Late life education and cognitive function in older adults
Bibliographic record
Abstract
OBJECTIVE: The potential role of education attained after the age of 50, for example, vocational training or recreational courses, in cognitive reserve has been unexplored. We examined the cross-sectional and prospective associations between late life education (LLE) and global cognitive function in older adults. METHODS: A total of 5306 participants (50+ years) in The Irish Longitudinal Study of Ageing answered questions about highest level of education completed and LLE (2010). Cognitive function was defined as the number of errors on the Montreal cognitive assessment (MoCA) assessed in 2010 and 2014. The association between LLE and MoCA-errors was examined using Poisson regression stratified by level of education. Sensitivity analyses were done to examine reverse causation and selection bias. RESULTS: In those with primary/no (n = 1312, incidence rate ratio [IRR] = 0.83, 95%CI = 0.70-0.99) and secondary education (n = 2208, IRR = 0.88, 95%CI = 0.80-0.97), but not tertiary education (n = 1786, IRR = 0.93, CI = 0.86-1.00), participating in LLE was associated with lower rate of MoCA errors. The prospective association between LLE and 4-year change in MoCA-errors was (borderline) statistically significant in those with primary/no education only (IRR = 0.86, CI = 0.74-1.00). Sensitivity analyses supported robustness of the findings. CONCLUSIONS: LLE may contribute to cognitive reserve and be a useful intervention to mitigate the increased risk of cognitive decline associated with low levels of education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".