Explaining Community Variation in Pesticide Use and Health Outcomes in Andean Ecuador
Bibliographic record
Abstract
ISEE-556 Objective: To determine cantonal/community factors that best explain intercommunity variation in a health outcome potentially related to pesticide use. Materials and Methods: We approached municipal, agricultural, and health stakeholders in 5 Ecuadorian cantons cultivating potato crops. We obtained existing data on cantons. Within these cantons, we selected 24 communities to conduct key informant interviews on community infrastructure. Within each community, we recruited about 20 households, interviewing 2 consenting adults per household (n=481); the person managing crops (usually male) and the person managing the household (usually female). Nursing assistants asked each individual about pesticide-related knowledge, practices, and health outcomes. Each individual performed a digit span test, (forward and back, converted to 0–10 scale). We constructed relevant scores and tested associations between blocks of related variables and digit span. Retained variables were included in a multilevel model for those adults that had a role with pesticides (n=544). Results: Mean pesticide usage per farm on all crops varied from <1 kg to >11 kg per crop cycle, much of it being WHO class Ib and II products. Digit span score across communities varied from 2.5 to 5.5. Intraclass correlation associated with communities was 7%. Canton and community variables alone explained 100% of this variation, household variables from 0 to 37%, and individual variables 11 to 17%. In multilevel analyses, digit score was associated with individual . Conclusions: Pro-poor agricultural development with fewer hazardous pesticide distributors could improve neurobehavioral function in resource poor farm households.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".