Disparities in the Deaths of Despair by Occupation, Massachusetts, 2000 to 2015
Bibliographic record
Abstract
OBJECTIVE: To explore mortality rates and trends according to the occupation of workers who died from the deaths of despair (DoD). METHODS: Death certificates for deaths due to poisonings (including opioid-related overdoses), suicides, and alcoholic liver disease occurring in Massachusetts from 2000 to 2015 were collected and coded according to the occupation of the decedent. Mortality rates and trends in mortality were calculated for each occupation. RESULTS: DoDs increased by more than 50% between 2000 to 2004 and 2011 to 2015. There were substantial differences in mortality rates and trends according to occupation. Blue collar workers were at a particularly elevated risk for DoD and had elevated trends for these deaths, notably: construction and farming, fishing, and forestry workers. CONCLUSIONS: Interventions should be targeted to occupations with elevated mortality rates and trends. Occupational risk factors that may contribute to these disparities should be explored.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".