Later-life employment trajectories and health
Bibliographic record
Abstract
Despite the recent policy push to keep older adults in the labour force, we know almost nothing about the potential health consequences of working longer. Drawing on a life course approach that considers stability and change in employment patterns, this study examines the relationship between long-term labour market involvement in later life and self-rated health. Our data are from the Health and Retirement Study (1992–2012) for the cohort born 1931–1941 (N = 6522). We used optimal matching analysis to map employment trajectories from ages 52–69, and then logistic regression to examine associations between these trajectories and self-rated health in the early 70s, net of socio-demographics, household resources and prior health. Women prevail in groups characterized by a weak(er) attachment to the labour market and men, in groups signifying a strong(er) attachment. Men who downshifted from full-time to part-time work around age 65 were the least likely to report poor health in their early 70s.Women had the best health if they remained employed, either full-time or part-time. However, unlike men, they appeared to benefit most in health terms when part-time hours were part of a longer-term pattern. While our study findings show that continuing to work in later life may be positively associated with health, they also suggest the need for flexible employment policies that foster opportunities to work part-time.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".