UNDERSTANDING THE RELATIONSHIP BETWEEN RETIREMENT AND COGNITIVE HEALTH
Bibliographic record
Abstract
Researchers have examined the association between cognition and retirement; however, results are inconsistent and the direction of the relationship is unclear. Risks may be context dependent. Retirement may be viewed by some as an opportunity to pursue interests and hobbies, whereas others may derive meaning and benefits from employment. Our purpose was twofold: 1) examine whether cognitive impairment predicts future employment status (i.e., retirement) and whether employment status predicts future cognitive impairment; and 2) explore predictors of cognitive impairment in employed, retired, and not employed individuals over the age of 50. We conducted secondary analyses of data from the first five waves of the English Longitudinal Study on Aging. In cross-lagged growth curve models (N=6492) adjusted for age, sex, education, social vulnerability, frailty, and baseline cognition or employment status, being retired was associated with better future cognitive function (b=-.19, p<.001) and a 10% unit increase in cognitive impairment was associated with lower odds of being retired in the future (OR=0.93, p<.05). In nonlagged growth curve models (N=10125), on average retired individuals had less cognitive impairment (b=-0.93, p<.01), but accumulated cognitive deficits more quickly than employed individuals. In general, cognitive deficits accumulated with age (b=0.64, p<.01); however, being employed and having higher education offered some protection. Increasing frailty was associated with faster cognitive decline. Retirement does not necessarily lead to decreasing cognitive function. Understanding the link between retirement and cognition can facilitate the development of appropriate interventions to help people maintain cognitive health in retirement.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".