Chemical and Physical Exposures in the Emerging US Green-Collar Workforce
Bibliographic record
Abstract
OBJECTIVE: "Green collar" workers serve in occupations that directly improve environmental quality and sustainability. This study estimates and compares the prevalence of select physical and chemical exposures among green versus non-green U.S. workers. METHODS: Data from the U.S. 2010 National Health Interview Survey (NHIS) Occupational Health Supplement were linked to the Occupational Information Network (ONET) Database. We examined four main exposures: 1) vapors, gas, dust, fumes (VGDF); 2) secondhand tobacco smoke; 3) skin hazards; 4) outdoor work. RESULTS: Green-collar workers were significantly more likely to report exposure to VGDF and outdoor work than nongreen-collar workers [adjusted odds ratio (AOR) = 1.25; 95% CI = 1.11 to 1.40; AOR = 1.44 (1.26 to 1.63), respectively]. Green-collar workers were less likely to be exposed to chemicals (AOR = 0.80; 0.69 to 0.92). CONCLUSIONS: Green-collar workers appear to be at a greater risk for select workplace exposures. As the green industry continues to grow, it is important to identify these occupational hazards in order to maximize worker health.
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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 | 0.000 | 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".