Educational Level, Pesticide Use, and Rice Farmers’ Health: A Survey in Sakassou Department, Côte d’Ivoire
Bibliographic record
Abstract
The improper use of pesticides, often linked to low educational levels among farmers, poses significant risks to both human health and the environment. This study examines the relationship between farmers’ educational attainment, pesticide use practices, and health impacts in Sakassou Department, Côte d’Ivoire. A survey was conducted from November to December 2022, involving 240 rice farmers selected through cluster sampling. Data were collected via individual interviews and analyzed using SPSS software. Results indicate that the majority of farmers are illiterate, limiting their ability to understand and apply pesticide safety guidelines. A strong correlation was found between education level and the likelihood of consulting instructional brochures, with more educated farmers being significantly more inclined to read and follow safety recommendations. Furthermore, farmers who consulted these brochures were six times more likely to adopt safer pesticide handling practices. To address these challenges, a targeted training program was implemented to educate farmers on proper pesticide use, including safe handling, application techniques, and post-treatment waste management. This initiative aimed to mitigate health risks and reduce environmental contamination. These findings underscore the urgent need for enhanced educational initiatives and tailored training programs to promote safer pesticide practices, thereby protecting both human health and ecosystems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".