8287710 CAREX Canada: perspectives on the use of population-based exposure surveillance programs for use in risk or burden estimates
Bibliographic record
Abstract
Objective CAREX Canada is a longstanding program of research that develops and disseminates estimates of the number of workers exposed to carcinogens. The objective of this talk is to offer a Canadian perspective on using population-level exposure estimates, with particular consideration of how these estimates have been used in Canadian risk and burden studies, and some of the benefits and limitations of these approaches. Material and Methods CAREX Canada began as a pilot program in 2003, based on methods developed in EU-based CAREX programs in the early 1990s. In 2008, it was supported by the cancer research arm of the Canadian government as a national program of research, who continue to fund it to this day. Using a mixed methods approach including key informant interviews, exposure measurement databases, and expert opinion, along with detailed census information, estimates have been generated for 2006 and 2016, with estimates for 2021 due to be released this fiscal year. Results Estimates of occupational exposure have been generated for >50 agents, many of which also have estimates of exposure level. They have contributed to many policy impacts, including a federal ban on asbestos in 2018 and a shifting of provincial approaches to reduce outdoor workers’ sun exposure. They have also been used in the national Burden of Occupational Cancer study for Canada, in addition to a number of epidemiological studies where CAREX estimates were converted into job exposure matrices. Conclusion CAREX Canada estimates have been used for policy influence and change, to raise awareness of occupational cancer for workers, their employers, and society more broadly, and have been translated into job exposure matrices for use in epidemiological investigators. While data like these are extremely important for understanding the extent of occupational cancer and disease, it is important to consider the limitations of these approaches.
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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 | 0.053 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".