Occupational Differences in Deaths of Despair in the United States, Using Data From the Using the National Occupational Mortality Surveillance System
Bibliographic record
Abstract
OBJECTIVE: To assess occupational differences in proportional mortality ratios (PMRs) and trends in these PMRs due to the deaths of despair in the United States. METHODS: PMRs for deaths due to drug overdoses, suicide, and alcoholic liver disease were obtained from the National Occupational Mortality Surveillance system. Data came from various states for the years 1985 to 1998, 1999, 2003 to 2004, and 2007 to 2014. RESULTS: Occupations with the highest risk for deaths of despair included construction; architects; and food preparation and service. Occupations with the highest increases in deaths due to deaths of despair included personal care and service and home aides. CONCLUSIONS: Identifying occupations with elevated risk factors for deaths of despair makes it possible to focus interventions on these occupations. Occupational hazards and exposures may increase risk to deaths of despair for specific workers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".