Health and Working Beyond Retirement Age: Exploring Racial and Gender Intersectionality
Bibliographic record
Abstract
Abstract Little longitudinal research exists on health and working among older racial and ethnic minority adults. Following previous cross-sectional research, we examine the Health, Aging, and Body Composition (HABC) study comparing working vs. not working overtime among older adults. We hypothesize: 1) Black vs. White adults are more likely to work; 2) Black vs. White differences in working are greater among women than men; and 3) Working relates to fewer prevalent health problems than not working. We used gender-stratified descriptive statistics and generalized mixed-effects logistic regression with covariate adjustments to analyze the HABC cohort study, with community-dwelling, well-functioning Black (42%) and White older adults aged 70-79 in year 1 (n=3,069) to year 6 (n=2,091). We found support for all three hypotheses. Black vs. White adults were more likely to work overtime. Women were less likely to work overtime compared to men. White women were less likely to keep working compared to men and Black women. Lastly, older adults with fewer chronic conditions were more likely to continue working. Our study finds racial and gender differences among older adults working overtime. Intersectionality plays a role in older adults’ health and work disparities, leading us to explore the needs and/or benefits of working past retirement in specific groups. Our policy implication is for society to pro-actively invest in older adults’ health and productive activities, which may act as social determinants of health solutions to reduce disparities and growing social safety net program costs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".