From Midlife to Early Old Age
Bibliographic record
Abstract
BACKGROUND: Previous studies report contradictory findings regarding health effects of retirement. This study examines longitudinally the associations of retirement with mental health and physical functioning. METHODS: The participants were 7584 civil servants from the Whitehall II cohort study aged 39-64 years at baseline and 54-76 years at the last follow-up. Self-reported mental health and physical functioning were assessed using the Short Form Medical Outcomes Survey questionnaire, and the scales were scored as T-scores (mean [SD] = 50 [10]). Retirement status and health were assessed with 6 repeated measurements over a 15-year period. RESULTS: The associations between retirement and health were dependent on age at retirement, reason for retirement, and length of time spent in retirement. Compared with continued employment, statutory retirement at age 60 and early voluntary retirement, respectively, were associated with 2.2 (95% confidence interval = 1.7 to 2.8) and 2.2 (1.7 to 2.7) points higher mental health and with 1.0 (0.6 to 1.5) and 1.1 (0.8 to 1.4) points higher physical functioning. Retirement due to ill health was associated with poorer mental health (-0.7 points [-1.62 to 0.2]) and physical functioning (-4.5 points [-5.1 to -3.9]). Within-subject analyses suggested a causal interpretation for statutory and voluntary retirement, but health selection for retirement due to ill health. CONCLUSIONS: Longitudinal analyses of repeat data suggest that health status improves after statutory and voluntarily retirement, although the improvement seems to attenuate over time. By contrast, the association between retirement due to ill health and subsequent poor health seems to reflect selection rather than causation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".