Bayesian Hierarchical Modelling of Individual Expert Assessments in the Development of a General-Population Job-Exposure Matrix
Bibliographic record
Abstract
The CANJEM job-exposure matrix compiles expert evaluations of 31 673 jobs from four population-based case-control studies conducted in Montreal. For each job, experts had derived indices of intensity, frequency, and probability of exposure to 258 agents. CANJEM summarizes the exposures assigned to jobs into cells defined by occupation/industry, agent, and period. Some cells may, however, be less populated than others, resulting in uncertain estimates. We developed a modelling framework to refine the estimates of sparse cells by drawing on information available in adjacent cells. Bayesian hierarchical logistic and linear models were used to estimate the probability of exposure and the geometric mean (GM) of frequency-weighted intensity (FWI) of cells, respectively. The hierarchy followed the Canadian Classification and Dictionary of Occupations (CCDO) classification structure, allowing for exposure estimates to be provided across occupations (seven-digit code), unit groups (four-digit code), and minor groups (three-digit code). The models were applied to metallic dust, formaldehyde, wood dust, silica, and benzene, and four periods, adjusting for the study from which jobs were evaluated. The models provided estimates of probability and FWI for all cells that pulled the sparsely populated cells towards the average of the higher-level group. In comparisons stratified by cell sample size, shrinkage of the estimates towards the group mean was marked below 5 jobs/cell, moderate from 5 to 9 jobs/cell, and negligible at ≥10 jobs/cell. The modelled probability of three-digit cells were slightly smaller than their descriptive estimates. No systematic trend in between-study differences in exposure emerged. Overall, the modelling framework for FWI appears to be a suitable approach to refine CANJEM estimates. For probability, the models could be improved by methods better adapted to the large number of cells with no exposure.
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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 | 0.002 | 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".