Quantifying farmers’ preferences for antimicrobial use for livestock diseases in northern Tanzania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it