Health Impacts of Pesticide Use and Associated Safety Practices among Farmers in A Rural Place in Palpa District of Nepal
Bibliographic record
Abstract
Introduction: The unsafe and indiscriminate use of pesticide in agriculture represents a major human health problem. The aim of this study was to assess the level of safety practices regarding pesticides use and its effect on health among farmers of Rampur municipality. Method: The research was a descriptive cross-sectional study in the rural area of the Lumbini Province and purposive sampling was used to collect data. The study assessed pesticide safety practices and health effects among 227 farmers in Rampur Municipality, Palpa. Result: While all farmers recognized pesticides as harmful, 79.29% used them in crops, and 96.5% had no formal training. Unsafe practices were common, including storing pesticides in living areas (26.11%) and improper disposal methods. Pesticide exposure occurred mainly via the oral route (48.5%), with 14.5% unaware of the entry route. Only 31.11% bathed fully after pesticide use. Despite high awareness, safety practices were poor. Conclusion: The study recommends safety training, stricter law enforcement, and promotion of integrated and non-synthetic pest control methods. Strengthening farmers’ access to protective equipment and ensuring routine monitoring and follow-up will further support safer pesticide handling and reduce long-term health risks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".