0050 Working towards assessing occupational carcinogenic exposures in an african lower and middle income country
Bibliographic record
Abstract
Aim We aim to use the Canadian CAREX (CARcinogen EXposure) tool, adapted for local context, as a method to assess prevalence and level of exposure to priority occupational carcinogens in South Africa. Methods The work entails first understanding the CAREX tool, and adapting it as well as reviewing its use in other countries (phase 1). Once the tool and database are prepared, we will gather publicly available data (i.e. Census data, information on chemical use, trade data, published and grey literature, expert consultation, etc.) on occupational exposure to carcinogens as well as exposure monitoring data (phase 2). We will consider all occupational health and safety legislation and its regulations regarding occupational exposure limits, and those carcinogens prioritised locally and internationally, for example by the International Agency for Research on Cancer. All data will be used to estimate the number of South African workers occupationally exposed to carcinogens (and where possible, their level of exposure) (phase 3). Ultimately, this will help guide the development of appropriate health promotion and worker protection programmes, among other resources aimed at cancer prevention (phase 4). Results Here we will present the experience of the team during phase 1 of the project, including challenges and opportunities encountered. Expected outcomes Key future outcomes include prevalence of exposure to occupational environmental carcinogens in the South African workplace; also proportions of the workforce in various occupational groups exposed to specific carcinogens; key occupational groups in need of protection; data and information that can be used to guide prevention programs.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".