Gender Differences in Occupational Exposure Assessment for a National Surveillance Project
Bibliographic record
Abstract
ISEE-0809 Background and Objective: The Finnish CAREX system is a useful tool for estimating numbers of workers exposed to carcinogens. It has been adapted for Canadian use and a new goal is to examine differences in occupational exposure by sex. Our objective is to identify circumstances where women may be underrepresented in original estimates. Methods: Population data was obtained from Statistics Canada. Finnish and US estimates of proportions of workers exposed to 9 carcinogens by industry and occupation were adapted for Canada with additional input from Canadian assessors (wood dust, benzene, silica, formaldehyde, diesel exhaust, PAH, chromium, lead, tetrachloroethylene). Numbers exposed were calculated by industry, occupation, province and sex. Descriptive statistics examining sex differences in industrial and/or occupational groups were prepared. Results: The proportion of men exposed ranged from 58% (tetrachloroethylene) to 94% (silica). There were only 3 carcinogens where men constituted <90% of workers exposed. In addition, the top industrial and occupational groups for men and women were different. For diesel exhaust, the largest industrial group for men was truck transportation, and the largest occupational group was truck drivers. For women, the largest industry was school bus transport, although this was only the 4th largest group for men. These results are unexpected; while we hypothesized that more men would be exposed to these substances, it is unlikely that they would account for >90% of exposed individuals. Further work is needed to ascertain whether this disparity is real, or if by using traditional methods of exposure assessment focusing on heavy industry, we have missed situations where women are exposed to carcinogens. Conclusion: CAREX did not originally discriminate between the sexes in terms of proportions of workers exposed. Our results suggest that industrial and/or occupational groups where women typically work may have been missed in initial estimates.
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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 | 0.003 | 0.001 |
| 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".