Characterizing the Burden of Occupational Chemical Exposures by Sociodemographic Groups in the United States, 2021
Bibliographic record
Abstract
Objectives. To estimate the number and prevalence of workers in the United States exposed to chemical hazards available in the Canadian job-exposure matrix (CANJEM) database and examine exposure disparities across sociodemographic groups. Methods. We merged US worker demographic data from the Current Population Survey with CANJEM to characterize the burden and sociodemographic distribution of 244 chemical exposures in the United States in 2021. An interactive version of the full data set is available online ( https://deohs.washington.edu/us-exposure-burden ). Results. Of the chemical exposures examined, the most prevalent were cleaning and antimicrobial agents (14.7% of workforce estimated exposed), engine emissions (12.8%), organic solvents (12.1%), polycyclic aromatic hydrocarbons (10.1%), and diesel engine emissions (8.3%). Racial and ethnic minoritized groups, persons with lower educational attainment, foreign-born noncitizens, and males were generally overrepresented in exposure to work-related chemical hazards. Conclusions. In the United States, marginalized sociodemographic groups are estimated to experience an inequitable burden to many chemical exposures because of occupational segregation. Data from this analysis can inform occupational and public health research, policy, and interventions aimed at reducing the burden of disease and health inequities in the United States. (Am J Public Health. 2024;114(1):57–67. https://doi.org/10.2105/AJPH.2023.307461 )
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".