Social differentials in older persons’ employment in Canada, Denmark, Sweden and the UK in 2010-15
Bibliographic record
Abstract
Background Rising life expectancy in the developed countries creates opportunities to extend working lives past 65 years yet a knowledge gap exists in understanding who already works past this age and any social differentials that may exist. Our study describes employment rates of those aged 65-75 years of age by educational level, health status and sex, across four different welfare states – Canada (CAN), Denmark (DK), Sweden (SE) and the United Kingdom (UK). The objective of the study is to identify country-level policies that encourage extended working lives. Methods The study examined employment rates for those aged 65-75 years by educational level, health status (having limiting longstanding illness (LLI) or not) and sex for the period 2010-2015 using nationally representative cross-sectional survey data from Canadian Community Health Survey, the Survey of Health, Ageing and Retirement in Europe for Denmark and Sweden and the English Longitudinal Study of Ageing. Results In all countries, employment rates were highest among high-educated healthy men (all countries over 25%) and lowest among low-educated women with LLI (all countries under 5%). Older UK workers are less likely to work past 65 years than those in CAN, DK and SE. For women, employment rates were highest in Sweden. Among persons with low education and LLI, women had considerably lower employment rates than men in all four countries (men: 21.6% in CAN; 11.1% in DK; 16.8% in SE and 4% in the UK compared to women: 3.8% in CAN; 2.5% in DK; 3.5% in SE and 3.4% in the UK). Conclusions In all countries, employment rates among adults aged 65-75 differed substantially by educational level and employment rates were lower among persons with health impairments across all education groups. There was little country variation in employment rates for low-educated unhealthy women, suggesting current policies in the four countries are not effective at mitigating social differentials in this vulnerable group. Key messages: Overall higher employment rates in Sweden suggest that social democratic policies may be better for extending working lives. Social differentials in employment rates exist across all four countries among working past 65 years, especially for unhealthy low-educated women.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".