Pesticide use behavior, cognitive impairment and the model of safety standard of pesticide use among farmworkers in Indonesia
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> The objective of this study was to identify pesticide residue, and to analyze associations between characteristics and pesticide use behavior with cognitive impairment among farmworkers. Additionally, a model of safety standard of pesticide use was constructed. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> <ns3:italic/> This observational study was conducted in Jember Regency, Indonesia using a cross-sectional design. Cluster random sampling was performed, whereby 500 farmworkers were selected as participants. Characteristics, pesticide use behavior and cognitive impairment data were collected using questionnaires. Bivariate and multivariate analyses were performed using chi-squared and logistic regression tests, respectively. The model was created by comparative analysis between theoretical concepts and empirical findings. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> <ns3:italic/> This study found 10/15 agricultural products contain Chlorpyrifos class of Organochlorin. Based on Montreal Cognitive Assessment, 70% of participants were in the category indicating cognitive impairment. Pesticide use behavior among farmworkers showed that 92.4% of participants were in the favorable category. The chi-squared test presented the significance of the type of commodity and pesticide storage associated with cognitive impairment among farmworkers (p<0.05). According to logistic regression analysis, other commodities (tobacco) (AOR: 0.21 (CI: 0.09–0.46)), pesticide exposure duration (AOR: 0.52 (CI:0.27–0.99)), pesticide dissemination at night (AOR:5.77 (CI:1.12-29.85)) and pesticide storage outdoors (AOR: 1.81 (CI:1.13–2.90)) were associated with cognitive impairment (p<0.05). The model of safety standard of pesticide use is constructed by four variables: farmworker behavior, personal protection equipment (PPE) utilization, implementing Integrated Pest Management (IPM) principal, and waste control. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> High numbers of agricultural products still contain pesticide residue. Although, pesticide use behavior was mainly within the favorable category, there were issues with excessive dose use, pesticide storage and disseminating time. The combination of favorable farmworker behavior, PPE utilization, implementing IPM principal, and waste control may protect farmworkers during pesticide use. The Agriculture District Office should conduct training workshops to farmworkers and provide waste management systems. </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".