Prevalence and Level of Occupational Exposure to Asbestos in Canada in 2016
Bibliographic record
Abstract
OBJECTIVE: Asbestos use has decreased over time but occupational exposure still exists today due to the presence of asbestos in older buildings. The objective of this study was to update CAREX Canada's prevalence of exposure estimate from 2006 to 2016, and to assess the level of occupational exposure by industry, occupation, province/territory, and sex. METHODS: Estimates by occupation, industry, province/territory, and sex were calculated using labor force data from the 2016 Census of Population and proportions of workers exposed by occupation and industry, which were previously developed for the 2006 estimates and updated here to reflect new knowledge and changes in exposures. Statistics Canada concordance tables were used to account for changes between the 2006 and 2016 job and industry coding systems. Expert assessment was used to qualitatively assign levels of exposure (low, moderate, or high) for each occupation and industry, with consideration of workers' proximity and access to asbestos-containing material, and the condition and content of asbestos. RESULTS: Approximately 235 000 workers are exposed to asbestos on the job in Canada. The majority of Canadian workers exposed to asbestos are male (89%). Only 5% of all exposed workers are in the high-exposure category, while most workers are in the low (49%) or moderate (46%) exposure categories. The construction sector and associated jobs (e.g. carpenters, trades helpers and laborers, electricians) accounted for the majority of exposed workers. CONCLUSIONS: Occupational exposure to asbestos continues to occur in Canada. Updating the prevalence of exposure estimate and adding exposure levels highlights the shift from high to lower-lever exposures associated with asbestos-containing materials remaining in the built environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".