Work‐related asthma in health care in Ontario
Bibliographic record
Abstract
BACKGROUND: The health of workers in health care has been neglected in the past. There are few reports regarding occupational asthma (OA) in this group, and work-exacerbated asthma (WEA) has rarely been considered. METHODS: We examined the frequency of claims for OA and WEA allowed by the compensation board in Ontario, Canada for which industry was coded as "health care" between 1998 and 2002, to determine the frequency of OA and WEA, causative agents, and occupations. RESULTS: During this period, five claims were allowed for sensitizer OA, two for natural rubber latex (NRL), and three for glutaraldehyde/photographic chemicals. The two NRL cases occurred in nurses who had worked for >10 years prior to "date of accident." There were 115 allowed claims for WEA; health care was the most frequent industry for WEA. Compared to the rest of the province, claims in health care made up a significantly greater proportion of WEA claims (17.8%) than OA (5.1%) (odds ratio, 4.1, 95% CI 1.6-11.6; P = 0.002). The rate of WEA claims was 2.1 times greater than that in the rest of the workforce (P < 0.0001). WEA claims occurred in many jobs (e.g., clerk), other than "classic" health care jobs such as nurses, and were attributed to a variety of agents such as construction dust, secondhand smoke, and paint fumes. CONCLUSIONS: WEA occurs frequently in this industrial sector. Those affected and attributed agents include many not typically expected in health care. The incidence of OA claims in this sector in general was low; the continued low number of OA claims due to NRL is consistent with the successful interventions for prevention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".