Unemployment Insurance and Mortality Among the Long-Term Unemployed: A Population-Based Matched-Cohort Study
Bibliographic record
Abstract
Unemployment insurance is hypothesized to play an important role in mitigating the adverse health consequences of job loss. In this prospective cohort study, we examined whether receiving unemployment benefits is associated with lower mortality among the long-term unemployed. Census records from the 2006 Canadian Census Health and Environment Cohort (n = 2,105,595) were linked to mortality data from 2006-2016. Flexible parametric survival analysis and propensity score matching were used to model time-varying relationships between long-term unemployment (≥20 weeks), unemployment-benefit recipiency, and all-cause mortality. Mortality was consistently lower among unemployed individuals who reported receiving unemployment benefits, relative to matched nonrecipients. For example, mortality at 2 years of follow-up was 18% lower (95% confidence interval (CI): 9, 26) among men receiving benefits and 30% lower (95% CI: 18, 40) among women receiving benefits. After 10 years of follow-up, unemployment-benefit recipiency was associated with 890 (95% CI: 560, 1,230) fewer deaths per 100,000 men and 1,070 (95% CI: 810, 1,320) fewer deaths per 100,000 women. Our findings indicate that receiving unemployment benefits is associated with lower mortality among the long-term unemployed. Expanding access to unemployment insurance may improve population health and reduce health inequalities associated with job loss.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".