P-336 Gender differences in occupational exposure in the Canadian job-exposure-matrix (CANJEM)
Bibliographic record
Abstract
Introduction One of the principal challenges in community-based occupational studies is retrospective assessment of exposure. Job-exposure-matrices (JEMs) have been proposed as a cost-effective tool. However, most JEMs are built from the assessment of jobs held by men, with few studies assessing the applicability of those JEMs to the same jobs when held by women. Objective To compare within-occupation exposure assessments for jobs held by men versus women in a Canadian JEM (CANJEM). Method Two sex-specific JEMs were created using data from CANJEM, a JEM based on the expert assessment of exposure to 258 chemicals in >30,000 jobs held during 1933–2005, by participants in five Montreal-based case-control studies. Each cell in the JEMs provided the probability, intensity, frequency, and frequency-weighted intensity (FWI) of exposure to a selected occupational agent, for a specific combination of a 4-digit Canadian Classification and Dictionary of Occupations code and one of four time periods. We used intra-class correlation coefficients (ICC) to compare the probability, frequency and FWI between cells considered exposed in the two JEMs; ‘exposed’ cells were defined by probability ≥ 5%. Given the semi-quantitative nature of intensity, Kendall’s Tau (τ) was used. We further compared differences between the JEMs using empirical cumulative distribution functions (ECDF). Results In total, 1,488 cells considered exposed in both JEMs were included in the analysis. ICC (95% confidence interval) of 0.58 (0.55–0.61), 0.55 (0.52–0.58) and 0.12 (0.07–0.17) were found for probability, frequency and FWI of exposure, respectively. For intensity, a τ of 0.24 (0.20–0.29) was found. The ECDFs showed a tendency for frequency to be higher in the female-JEM, but for intensity and FWI to be higher in the male-JEM. No clear trend was observed for probability of exposure. Conclusion Our results suggest that CANJEM assessment of jobs held by men was inadequate to estimate exposure in women’s.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".