Does retirement influence cognitive performance? The Whitehall II Study
Bibliographic record
Abstract
BACKGROUND: Occupational work involves many factors capable of protecting cognition. The 'disuse' hypothesis suggests that removal of such factors at retirement may increase the risk of cognitive decline. OBJECTIVE: To examine whether retirement is significantly associated with cognitive change after adjusting for preretirement cognitive function, personal, social, health and lifestyle factors, work characteristics and leisure activity. METHODS: participants were from the Whitehall II study, a prospective study of London-based Civil Servants. Short-term memory, the AH4 Part 1 (a test of inductive reasoning), verbal fluency and the Mill Hill Vocabulary Scale were collected at ages 38-60 years, and again, on average 5 years later, at 42-67 years, providing pre- and postretirement cognitive functioning assessments for 2031 participants (470 retired and 1561 working). Linear regression was used to test the association between retirement and cognitive performance adjusted for preretirement cognition. RESULTS: Mean cognitive test scores increased between the two assessments. However, after adjusting for age, sex, education, occupational social class, Mill Hill score, work characteristics, leisure activities, and indicators of physical and mental health, those retired showed a trend towards smaller test score increases over 5 years than those still working, although this only reached 5% significance in one test (AH4; β=-0.7, 95% CI -1.2 to -0.09) and did not show a dose-response effect with respect to length of time in retirement. CONCLUSIONS: This trend is consistent with the disuse hypothesis but requires independent replication before it can be accepted as supportive in this respect.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".