Estimating National-Level Exposure to Antineoplastic Agents in the Workplace: CAREX Canada Findings and Future Research Needs
Bibliographic record
Abstract
OBJECTIVES: Occupational exposure to antineoplastic agents occurs in various environments and is associated with increased cancer risk and adverse reproductive outcomes. National-level information describing the location and extent of occupational exposure to antineoplastic agents is unavailable in Canada and most other countries. CAREX Canada aimed to estimate the prevalence and relative levels of occupational exposures to antineoplastic agents across work setting, occupation, and sex. METHODS: 'Exposure' was defined as any potential for worker contact with antineoplastic agents. Baseline numbers of licensed workers were obtained from their respective professional bodies. For unlicensed workers, Census data or data extrapolated from human resources reports (e.g., staffing ratios) were used. Prevalence was estimated by combining population estimates with exposure proportions from peer-reviewed and grey literature. Exposure levels (classified as low, moderate, and high) by occupation and work setting were estimated qualitatively by combining estimates of contact frequency and exposure control practices. RESULTS: Approximately 75000 Canadians (0.42% of the total workforce) are estimated as occupationally exposed to antineoplastic agents; over 75% are female. The largest occupational group exposed to antineoplastic agents is community pharmacy workers, with 30200 exposed. By work setting, 39000 workers (52% of all exposed) are located in non-hospital settings; the remaining 48% are exposed in hospitals. The majority (75%) of workers are in the moderate exposure category. CONCLUSIONS: These estimates of the prevalence and location of occupational exposures to antineoplastic agents could be used to identify high-risk groups, estimate disease burden, and target new research and prevention activities. The limited secondary data available for developing these estimates highlights the need for increased quantitative measurement and documentation of antineoplastic agent contamination and exposure, particularly in work environments where use is emerging.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".