Agreement in Occupational Exposures Between Men and Women Using Retrospective Assessments by Expert Coders
Bibliographic record
Abstract
Objectives: To estimate the level of agreement and identify notable differences in occupational exposures (agents) between men and women from retrospective assessments by expert coders. Methods: Lifetime occupational histories of 1657 men and 2073 women from two case-control studies, were translated into exposure estimates to 243 agents, from data on 13882 jobs. Exposure estimates were summarized as proportions and frequency-weighted intensity of exposure for 59 occupational codes by sex. Agreement between metrics of exposure in men's and women's jobs was determined with intraclass correlation coefficients (ICC) and weighted Kappa coefficients, using as unit of analysis ('cell') a combination of occupational code and occupational agent. 'Notable' differences between men and women were identified for each cell, according to a Bayesian hierarchical model for both proportion and frequency-weighted intensity of exposure. Results: For cells common to both men and women, the ICC for continuous probability of exposure was 0.84 (95% CI: 0.83-0.84) and 7.4% of cells showed notable differences with jobs held by men being more often exposed. A weighted kappa of 0.67 (95% CI: 0.61-0.73) was calculated for intensity of exposure, and an ICC of 0.67 (95% CI: 0.62-0.71) for frequency-weighted intensity of exposure, with a tendency of higher values of exposure metrics in jobs held by men. Conclusions: Exposures were generally in agreement between men and women. Some notable differences were identified, most of them explained by differential sub-occupations or industries or dissimilar reported tasks within the studied occupations.
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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 | 0.037 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".