Zoonotic diseases awareness and food safety practices among livestock farmers in Nepal
Bibliographic record
Abstract
Interactions between humans and livestock could increase the risk of zoonotic disease transmission. In addition, limited knowledge of zoonoses and foodborne diseases among livestock farmers could heighten the risks of foodborne illness and outbreaks of zoonotic diseases. This study evaluated the awareness of zoonotic diseases and preventive practices for zoonotic and foodborne diseases among livestock farmers of the Chitwan, Rupandehi, and Tanahun districts of Nepal by conducting a cross-sectional survey of 280 livestock farmers. They were recruited using the purposive sampling method from October to December 2022. Descriptive statistics revealed that most (72.1%; n = 202/280) livestock farmers were aware of zoonosis. None of the farmers knew about the zoonotic nature of leptospirosis. Two-thirds of pig farmers (67%; n = 12/18) were aware of zoonotic transmission of swine flu, and more than half of the poultry (58%; 50/86) farmers knew about zoonotic avian influenza. The majority of the farmers who had dogs (83%) and cats (89.4%) in their homes or farms knew that rabies can be transmitted to humans from dogs or cats. The multivariable logistic regression analysis revealed that farmers from the Rupandehi district (aOR: 5.56; 95% CI: 2.18–14.22) and Chitwan (aOR: 6.52; 95% CI: 2.46–17.25) had a higher odds of having good preventive practices than those from Tanahun. Also, farmers who had no sickness in the past 6 months after consumption of animal products were three times (aOR: 2.98; 95% CI: 1.48–6.01) more likely to have better practices. Furthermore, secondary education (aOR: 3.64; 95% CI: 1.41–9.44) was a significant positive predictor of good zoonotic diseases and food safety preventive practices. Our study underscores the necessity to enhance Nepalese livestock farmers’ awareness and practices regarding zoonotic and foodborne diseases. It emphasizes the importance of understanding risks, effective behavioral change strategies, and engaging farmers in developing zoonotic disease and foodborne illness prevention programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".