Employment quality and suicide, drug poisoning, and alcohol-attributable mortality
Bibliographic record
Abstract
Suicide, drug poisoning, and alcohol-attributable mortality (SDAM)-often labeled "deaths of despair"-are increasing among working-aged individuals in many high-income countries. We examined the association between employment quality and SDAM in Canada. Census records from the 2006 Canadian Census Health and Environment Cohort (n = 2 805 550) were linked to mortality data from 2006 to 2019. Latent class analysis identified 5 employment quality types: standard (secure and rewarding), portfolio (rewarding but demanding), marginal (limited hours and earnings), intermittent (sporadic and unstable), and precarious (insecure and unrewarding). Poisson regression models estimated sex/gender-stratified associations between employment quality type and SDAM separately. We observed a consistent mortality gradient across employment quality groups, with lower-quality employment-and precarious employment in particular-associated with increased rates of SDAM relative to higher-quality (ie, standard) employment. For example, precarious employment was associated with a more than 3-fold rate of drug poisoning deaths among women (rate ratio [RR] = 3.58; 95% CI, 3.21-4.00) and a more than 2-fold rate of alcohol-attributable death among men (RR = 2.22; 95% CI, 2.07-2.38). Employment quality is an important determinant of SDAM, with varying associations by sex/gender. Improvements in employment conditions may help reduce the burden of premature deaths attributable to suicide and substance use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".