Bibliographic record
Abstract
This special issue of Occupational Medicine is dedicated to occupational medicine in Canada. Occupational Medicine has been for many years the adopted journal of the Occupational and Environmental Medical Association of Canada (OEMAC) and close collaboration between the journal and Canadian occupational physicians is encouraged. The papers in this issue describe various aspects of occupational medicine research and practice in Canada. Several of the papers describe studies involving linkage of large databases to investigate workplace aetiology. In each Canadian province, residents are covered by a provincially administered heath care plan and there is a single workers’ compensation board (WCB) that covers the majority of workers, which provide rich data sources for linkage. The study by Cherry et al. [1] demonstrates the utility of this type of data linkage to examine the relationship between occupation and new-onset physician-diagnosed mental ill-health. Information on occupation was obtained on all accepted WCB claims (irrespective of the type of injury or illness giving rise to the claim) during a 10-year period and was linked to administrative health records to obtain a diagnosis by a physician of new-onset mental ill-health (affective disorders, substance abuse, psychotic disorders), occurring within 12 months of the WCB claim. The results of their analysis suggest several areas for future study or intervention. For example substance use disorders were found to cluster mainly in physically demanding occupations often involving employment outside urban areas.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.045 | 0.028 |
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".