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Record W2085968573 · doi:10.1002/ajim.20935

Work‐related asthma in health care in Ontario

2011· article· en· W2085968573 on OpenAlexaffabout
Gary M. Liss, Larisa Buyantseva, Carol E. Luce, Marcos Ribeiro, Michael Manno, Susan M. Tarlo

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsWorkplace Safety & Insurance BoardSinai Health SystemUniversity of TorontoOntario Ministry of Labour
Fundersnot available
KeywordsMedicineHealth careWorkforceAsthmaOccupational asthmaEnvironmental healthOccupational medicineOccupational exposureInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.046
GPT teacher head0.290
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2011
Admission routes2
Has abstractyes

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