The role of income and occupation in the association of education with healthy aging: results from a population-based, prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The beneficial effects of higher education on healthy aging are generally accepted, but the mechanisms are less well understood. Education may influence healthy aging through improved employment opportunities that enhance feelings of personal control and reduce hazardous exposures, or through higher incomes that enable individuals to access better health care or to reside in better neighbourhoods. Income and occupation have not been explored extensively as potential mediators of the effect of education on healthy aging. This study investigates the role of income and occupation in the association between education and healthy aging including potential effect modification by gender. METHODS: Logistic regression was used to explore the association of education, income (perceived income adequacy, life satisfaction with finances) and occupation (occupational prestige) with healthy aging five years later in 946 community-dwelling adults 65+ years from a population-based, prospective cohort study in Manitoba, Canada. RESULTS: Higher levels of education generally increased the likelihood of healthy aging. After adjusting for education, both income measures, but not occupation, predicted healthy aging among men; furthermore, the association between education and healthy aging was no longer significant. Income and occupation did not explain the significant association between education and healthy aging among women. CONCLUSIONS: Perceived income adequacy and life satisfaction with finances explained the beneficial effects of higher education on healthy aging among men, but not women. Identifying predictors of healthy aging and the mechanisms through which these factors exert their effects can inform strategies to maximize the likelihood of healthy aging.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".