Estimating the population prevalence of traditional and novel occupational exposures in Federal Region X
Bibliographic record
Abstract
OBJECTIVE: Federal Region X is an administrative region in the northwestern United States comprised of the states of Alaska (AK), Idaho (ID), Oregon (OR), and Washington (WA). Quantifying the number of workers in this region exposed to harmful circumstances in the workplace, and projected changes over time will help to inform priorities for occupational health training, risk reduction, and research. METHODS: State data for WA, ID, OR, and AK were used to estimate number of workers by occupation, in 2014 and 2024. These data were merged with a Canadian job-exposure matrix (CANJEM) which characterizes chemical exposures, and O*NET, which ranks occupations with particular physical, ergonomic, and psychosocial exposures. RESULTS: Of the exposures considered, psychosocial and ergonomic exposures were the most prevalent among the regional workforce, though traditional chemical exposures are still common and increasing. CONCLUSIONS: Exposure surveillance will inform prioritization of risk reduction strategies, ultimately leading to a decrease in occupational injury and illness. Findings from this analysis will help to prioritize occupational health training and research in the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".