The social biography of antibiotic use in smallholder dairy farms in India
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) has been identified as one of the major threats to global health, food security and development today. While there has been considerable attention about the use and misuse of antibiotics amongst human populations in both research and policy environments, there is no definitive estimate of the extent of misuse of antibiotics in the veterinary sector and its contribution to AMR in humans. In this study, we explored the drivers ofirrational usage of verterinary antibiotics in the dairy farming sector in peri-urban India. Methods and materials: The study was conducted in the peri-urban belts of Ludhiana, Guwahati and Bangalore. A total of 54 interviews (formal and non-formal) were carried out across these three sites. Theme guides were developed to explore different drivers of veterinary antimicrobial use. Data was audio recorded and transcribed. Analysis of the coded data set was carried out using AtlasTi. Version 7. Themes emerged inductively from the set of codes. Results: Findings were presented based on concept of 'levels of analyses'. Emergent themes were categorised as individual, health systems, and policy level drivers. Low level of knowledge related to antibiotics among farmers, active informal service providers, direct marketing of drugs to the farmers and easily available antibiotics, dispensed without appropriate prescriptions contributed to easy access to antibiotics, and were identified to be the possible drivers contributing to the non-prescribed and self-administered use of antibiotics in the dairy farms. Conclusions: Smallholding dairy farmers operated within very small margins of profits. The paucity of formal veterinary services at the community level, coupled with easy availability of antibiotics and the need to ensure profits and minimise losses, promoted non-prescribed antibiotic consumption. It is essential that these local drivers of irrational antibiotic use are understood in order to develop interventions and policies that seek to reduce antibiotic misuse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".