Healthy working life expectancy across birth cohorts in the United States
Bibliographic record
Abstract
OBJECTIVES: This study investigates trends in healthy working life expectancy (HWLE) in the United States amid changing retirement conditions and recent declines in health among working-age individuals. HWLE, defined as the average number of years expected to be spent healthy and working between ages 51 and 80, is examined by gender and educational level across three birth cohorts. METHODS: Using longitudinal data from the Health and Retirement Study (HRS), HWLE estimates were calculated for individuals aged 51 and older. Using continuous-time multistate modelling, trends were analyzed across three birth cohorts (1936-1941, 1942-1947, 1948-1953), focusing on differences by gender and educational attainment to assess disparities in HWLE over time. RESULTS: The findings indicate that HWLE remained stable for most groups but declined among individuals with lower educational attainment. Thus, as early retirement becomes increasingly costly and risky, workers appear unable to extend their working lives and are facing growing inequalities. DISCUSSION: These findings highlight the need for targeted policies to promote healthier work environments and expanded job opportunities for older adults. Addressing disparities in HWLE, particularly for those with less education, is critical to improving outcomes for future cohorts as they approach retirement age.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".