Quantifying farmers’ preferences for antimicrobial use for livestock diseases in northern Tanzania
Bibliographic record
Abstract
Abstract Understanding the choice behaviours of farmers around the treatment of their livestock is critical to counteracting the risks of antimicrobial resistance (AMR) emergence. Using varying disease scenarios, we measure the differences in livestock species’ treatment preferences and the effects of context variables (such as grazing patterns, herd size, travel time to agrovet shops, previous disease experience, previous vaccination experience, education level, and income) on the farmers’ treatment choices for infections across three production systems—agro-pastoral, pastoral, and rural smallholder—in northern Tanzania, where reliance on antimicrobial treatment to support the health and productivity of livestock is high. Applying a context-dependent stated choice experiment, we surveyed 1224 respondents. Mixed logit model results show that farmers have higher preferences for professional veterinary services when treating cattle, sheep, and goats, while they prefer to self-treat poultry. Antibiotics sourced from agrovet shops are the medicine of choice, independent of the health condition to treat, whether viral, bacterial, or parasitic. Nearness to agrovet shops, informal education, borrowing and home storage of medicines, and commercial poultry rearing increase the chances of self-treatment. Based on our findings, we propose interventions such as awareness and education campaigns aimed at addressing current practices that pose AMR risks, as well as vaccination and good livestock husbandry practices, capacity building, and provision of diagnostic tools.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".