Antibiotic use in pig farming and its associated factors in L County in Yunnan, China
Bibliographic record
Abstract
China has a long history of pig rearing, and it currently raises and consumes approximately half of the pigs in the world. Major improvements have been made in pig farming in China in the last four decades with the growing application of new livestock farming technologies. Among the new improvements, the use of antibiotics in pig farming is a common but not well-documented practise. In order to understand the behaviour of the farmers regarding antibiotic use in pig farming, we conducted a household survey in four townships of L County in Yunnan Province, China, during August 2014 and April 2015. In this survey, 404 farmer households were interviewed using a questionnaire. Among the farmers interviewed, 89% reported easy access to antibiotics, 83.7% reported experience of self-purchasing antibiotics, and 40.3% reported that they often used antibiotics in pig farming mainly for the prevention and treatment of pig diseases. These farmers identified 20 antibiotics that they had used in pig farming 6 months before the survey. Of these, 11 and 8 antibiotics have been categorised under 'critically important' and 'highly important' antimicrobial groups, respectively, by the World Health Organization (WHO), and 12 and 8 have been categorised under the 'Watch' and 'Access' groups, respectively, as per the 2019 WHO AWaRe classification of antibiotics. Factors associated with the behaviour of self-purchasing antibiotics included types of farms, sources of antibiotics, and previous experiences of pig diseases: those who were smallholders, buying antibiotics from veterinary drugstores and village vets, and whose pigs had suffered diseases previously were more likely to self-purchase antibiotics for their pigs. Farmers who cleaned their pigsties less frequently and those whose pigs had suffered from diseases used antibiotics more frequently as compared to their peer farmers.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".