MétaCan
Menu
Back to cohort
Record W1717900500 · doi:10.1002/ajim.22428

Prevalence of occupational exposure to carcinogens among workers of Arabic, Chinese and Vietnamese ancestry in Australia

2015· article· en· W1717900500 on OpenAlexaff
Terry Boyle, Renee N. Carey, Deborah C. Glass, Susan Peters, Lin Fritschi, Alison Reid

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersNational Health and Medical Research CouncilAustralian Research Council
KeywordsMedicineVietnameseEnvironmental healthEthnic groupOccupational exposureArabicPopulationOccupational safety and healthDemographyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Although job-related diseases result in more deaths per year than job-related injuries, most research concerning ethnic minority workers has concerned accidents and injuries rather than disease-causing exposures such as carcinogens. METHODS: We conducted a telephone-based cross-sectional survey to estimate the prevalence of occupational exposure to carcinogens among a sample of ethnic minority workers in Australia, and compared their exposure prevalence to that of a sample of the general Australian-born working population ('Australian workers'). RESULTS: One-third of the ethnic minority workers were exposed to at least one carcinogen at work. The likelihood of exposure to carcinogens was not significantly different from that of Australian workers, although the likelihood of exposure to individual carcinogens varied by ethnicity. CONCLUSION: Knowing the prevalence of exposure to carcinogens in the workplace in different ethnic groups will allow better targeted and informed occupational health and safety measures to be implemented where necessary.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

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

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 head0.079
GPT teacher head0.349
Teacher spread0.270 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Industrial MedicineSame topicOccupational and environmental lung diseasesFrench-language works237,207