Availability of a New Job-Exposure Matrix (CANJEM) for Epidemiologic and Occupational Medicine Purposes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to introduce the Canadian job-exposure matrix (CANJEM). METHODS: Four large case-control studies of cancer were conducted in Montreal, focused on assessing occupational exposures by means of detailed interviews followed by expert assessment of possible occupational exposures. Thirty-one thousand six hundred seventy-three jobs were assessed using a checklist of 258 agents (listed with prevalences at http://expostats.ca/chems). This large exposure database was configured as a JEM. RESULTS: CANJEM is available in four occupational classification systems. It provides estimates of probability of exposure among workers with a given occupation, and for those exposed, various metrics of exposure. CANJEM can be accessed online (www.canjem.ca) or in a batch version. CONCLUSION: CANJEM is a large source of retrospective exposure information, covering most occupations and many agents. CANJEM can be used to support exposure assessment efforts in epidemiology and occupational 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".