Para-occupational exposure to pesticides in agricultural families: developing models for risk assessment
Bibliographic record
Abstract
Objectives We aim to construct para-occupational pesticide exposure models to refine exposure estimates within farming communities. Para-occupational exposure reflects the experiences of people who may not formally work on farms, but live on or near sprayed areas or participate in unpaid farmwork. The first step of this process was a comprehensive review documenting the extent and main pathways of para-occupational exposures. Methods A literature search was undertaken. Papers examining para-occupational exposure in North American farmer/farmworker families were included, as the population of interest. Environmental, biological and epidemiological data were critically examined and catalogued. Results The studies examined showed increased pesticide exposure for farm children compared to non-farm children. Children who were present during spraying showed even higher levels of exposure. A smaller but similar increase was seen for farm spouses. Major pathways of exposure included workers9 boots, clothes and vehicles. Proximity of the home to spraying was also a factor. Epidemiological studies showed higher rates/risks of cancer and decreased neurobehavioral performance in children whose parents were occupationally exposure to pesticides. Conclusions Para-occupational pathways are complex and reflect a mixture of direct contact (inhalation of drift) and indirect exposure (residue build-up in carpets, vehicles and laundry). An attempt to delineate the magnitude of these different pathways was made by Arcury et al (2007) but more precise exposure data was needed. We propose stratified models for children and spouses that reflect direct and indirect sources, which will generate a more refined assessment of exposures occurring within North American farming communities.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".