Diabetes Associated With Early Labor-Force Exit: A Comparison Of Sixteen High-Income Countries
Bibliographic record
Abstract
The economic burden of diabetes and the effects of the disease on the labor force are of substantial importance to policy makers. We examined the impact of diabetes on leaving the labor force across sixteen countries, using data about 66,542 participants in the Survey of Health, Ageing and Retirement in Europe; the US Health and Retirement Survey; or the English Longitudinal Study of Ageing. After matching people with diabetes to those without the disease in terms of age, sex, and years of education, we used Cox proportional hazards analyses to estimate the effect of diabetes on time of leaving the labor force. Across the sixteen countries, people diagnosed with diabetes had a 30 percent increase in the rate of labor-force exit, compared to people without the disease. The costs associated with earlier labor-force exit are likely to be substantial. These findings further support the value of greater public- and private-sector investment in preventing and managing diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".