ACCESS TO COGNITIVE RESOURCES IN EARLY LIFE AND TRANSITIONS ACROSS COGNITIVE STATES IN MIDDLE AND OLDER ADULTHOOD
Bibliographic record
Abstract
An expanding body of research highlights that a cognitively engaged lifestyle may protect against neurodegeneration in late life (e.g., thereby promoting cognitive reserve). The majority of research in this area uses education as a proxy to investigate the cognitive reserve hypothesis; however, more recent findings suggest that other factors from the lifespan contribute to cognitive reserve. The current study examined whether access to cognitive resources in childhood (e.g., number of books at home) influenced healthy and impaired life expectancies using coordinated analyses of data from eight countries. Multistate survival models examined transitions between healthy, mild, and severely impaired cognitive states and death, which were used to compute life expectancies for individuals with and without impairment. Controlling for age, sex, education, SES, and country, analyses revealed a pattern consistent with the cognitive reserve hypothesis. Specifically, having access to more cognitive resources in childhood was associated with compression of morbidity—longer life expectancy free of impairment and less time spent with cognitive impairment. Cross-country differences in this effect will be discussed. Overall, these findings underscore the importance of childhood cognitive stimulation for cognitive health in later life and emphasize the need for a lifespan-focused approach to studying cognitive reserve.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".