Health promotion outcomes associated with a community-based program to reduce pesticide-related risks among small farm households
Bibliographic record
Abstract
A range of determinants at multiple socio-ecological levels operate in small farm households' use and handling of hazardous pesticides, suggesting the need for integrated health and agriculture promotion approaches. The aim is to assess changes in health promotion outcomes relevant to highly hazardous pesticide use associated with a multi-component community program. A longitudinal evaluation design using mixed methods was employed in 18 agricultural communities in Ecuador. Over a 7-month period, health education and agricultural interventions focused upon: health risks associated with hazardous pesticides, more adequate use and handling of pesticides, and better crop management techniques. Data collection included field forms, focus groups, structured observations and repeat surveys. In the qualitative analysis, communities were compared by extent of leadership and involvement with the interventions. For the quantitative analysis, hypothesized paths were constructed including factors relevant to pesticide-related practices and use. Testing involved gender-role stratified (household and crop manager) multivariable regression models. Information on pesticide health impacts and the pesticide use and handling, shared in focus groups, showed substantial improvement, as a result of health promotion activities though people were still observed to engage in risky practices in the field. In path models, community leadership and intervention intensity lead to changes in the household managers' pesticide-related knowledge and practices and to reduction in farm use of hazardous pesticides (both significant, p < 0.05). Integrated, community programs can promote pesticide-related risk reduction among small farm households. Changing practices in the use and management of pesticides among crop managers appears limited by deeper structural and cultural factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".