O-180 CAREX Canada: prevalence and level of occupational asbestos exposure in Canada in 2016
Bibliographic record
Abstract
Introduction Despite a federal asbestos ban, occupational asbestos exposure persists in Canada due to asbestos in older buildings and other legacy products or lingering imported materials. We updated CAREX Canada’s prevalence of exposure estimate from 2006 to 2016, and assessed the level of occupational exposure by industry, occupation, province/territory, and sex. Material and Methods Labour force data from the 2016 Census of Population and proportions of workers exposed by occupation and industry, which were previously developed for 2006 and updated here to reflect new knowledge and changes in exposures, were combined to estimate exposure by occupation (4-digit 2016 NOC), industry (4-digit 2012 NAICS), province/territory, and sex. Changes between the 2006 and 2016 job and industry coding systems were accounted for using Statistics Canada concordance tables. Levels of exposure (low, moderate, high), were qualitatively assigned for each occupation and industry intersection using expert assessment, considering workers’ proximity and access to asbestos-containing material, and the condition and content of asbestos. Results Approximately 235,000 workers (1.5%) are occupationally exposed to asbestos in Canada in 2016. Most are male (89%) and in the low (49%) or moderate (46%) exposure categories. The construction sector and associated jobs (e.g. carpenters, trades helpers and laborers) account for the majority; an estimated 157,000 workers are exposed in the industry, followed by public administration (29,000) and health care and social assistance (19,000). Other occupations with exposed workers include janitors, caretakers, and building super intendents (19,000) and light duty cleaners (12,000). The estimated prevalence of workers exposed increased from 2006 to 2016 due to increases in the labour force and the addition of some previously unrecognized groups. Conclusions Workers continue to be exposed to asbestos in Canada. Our results illustrate the shift from high exposures to lower-level exposures, which are associated with remaining asbestos-containing materials 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| 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) | 0.006 | 0.001 |
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".