Examination of urinary pesticide concentrations, protective behaviors, and risk perceptions among Latino and Latina farmworkers in Southwestern Idaho
Bibliographic record
Abstract
INTRODUCTION: Studies have documented high levels of pesticide exposure among men farmworkers; however, few have examined exposures or the experiences of women farmworkers. Data gaps also exist regarding farmworkers' perceived risk and control related to pesticides, information that is critical to develop protective interventions. OBJECTIVE: We aimed to compare urinary pesticide biomarker concentrations between Latino and Latina farmworkers and examine associations with occupational characteristics, risk perceptions, perceived control, and protective behaviors. METHODS: We enrolled a convenience sample of 62 farmworkers (30 men and 32 women) during the pesticide spray season from April-July 2022 in southwestern Idaho. Participants were asked to complete two visits within a seven-day period; at each visit, we collected a urine sample and administered a questionnaire assessing demographic and occupational information. Urine samples were composited and analyzed for 17 biomarkers of herbicides and of organophosphate (OP) and pyrethroid insecticides. RESULTS: Ten pesticide biomarkers (TCPy, MDA, PNP, 3-PBA, 4-F-3-PBA, cis- and trans-DCCA, 2,4-D, Glyphosate, AMPA) were detected in >80% of samples. Men and women had similar urinary biomarker concentrations (p = 0.19-0.94); however, women worked significantly fewer hours than men (p = 0.01), wore similar or greater levels of Personal Protective Equipment (PPE), and were slightly more likely to report having experienced an Acute Pesticide Poisoning (26% of women vs. 14% of men; p = 0.25). We observed inconsistencies in risk perceptions, perceived control, and protective behaviors among men. DISCUSSION: Our study is one the first to examine pesticide exposure and risk perceptions among a cohort of farmworkers balanced on gender. Taken with previous findings, our results suggest that factors such as job tasks, biological susceptibility, or access to trainings and protective equipment might uniquely impact women farmworkers' exposure and/or vulnerability to pesticides. Women represent an increasing proportion of the agricultural workforce, and larger studies are needed to disentangle these findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".