The Health Belief Model in Prevention Pesticide Toxicity
Bibliographic record
Abstract
Various efforts have been made to reduce pesticide toxicity, but the level of community participation is still quite low. This study aims to analyze the health belief of Karo’s farmer in pesticide toxicity prevention. The sample used the snowball sampling technique and reach 55 participants. Data were collected by in-depth interviews, FGD, and analyzed in qualitative used thematic analysis. The results found that farmers knew the hazard and effects of pesticides, but they ignored all of prevention. They continue to survive using pesticides cause indirect effects and temporary form of pesticides. They will refer to health services if it was eaten or inhaled, with acute effects and this is very rare. These perceived of farmer showed no benefit to prevent the pesticide. Overall, the farmer will participate in the health programmed if it has the real object and has significant changes to the economic and welfare of farmers. It concluded that the model of health belief could be changing the health behavior in pesticide use influences by the pesticide hazard, fluctuating of market price and horticulture products in bigger demand, traditional medicine habit, and government assurance to farmers.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".