Exposure to Pesticide and Its Association With Respiratory Health Among Paddy Farmers at TanjungKarang, Selangor
Bibliographic record
Abstract
Pesticide has been used by paddy farmers in their agricultural activities to increase rice crop yield since past few decades. However, the usage of this chemical with limited knowledge of its deleterious effects on health, community and environment without proper consideration of safety may cause chronic health problems such as respiratory health problems and lung dysfunction. This paper was conducted to determine the association of exposure to pesticide with respiratory health among paddy farmers at TanjungKarang, Selangor as exposed group and office workers working in TanjungKarang town as the comparative group. The results showed that the duration of exposure to pesticide between both groups were mostly low (86%) and high duration for only 4% for paddy farmers and none of office workers. There were significant differences in cough and phlegm, as they were higher among paddy farmers compared to the comparative group at ρ<0.001. Paddy farmers who were exposed to high levels of pesticide were 2 times more likely to have cough and 3 times more likely to get phlegm. FVC% (t=-1.470, p=0.001) and FEV1% (t=-1.526, p=0.001) were lower among the exposed group compared to the comparative group. The prevalence of abnormal FVC were found at 80% in exposed group and 38% in comparative group andabnormal FEV1 were found at 89% and 31% of respondents in both study groups. There was a significant correlation between long abnormalities for FVC% and exposure duration (χ2= 2.903, p= 0.001). This study concluded that the paddy farmers were at risk of respiratory symptoms, as reflected by the increase in the reported respiratory symptoms and lung function reduction.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".