Situational analysis of human and agricultural health practice: One Health and antibiotic use in an indigenous village in rural Punjab, India
Bibliographic record
Abstract
Antimicrobial resistance (AMR) represents one of the biggest threats to health globally. The rise of AMR has been largely attributed to the misuse and abuse of antimicrobials in veterinary, human, and agricultural medicine. This study aimed to assess human, livestock, and agricultural health profiles, and practices of One Health and antibiotic use through a situational analysis of an Indigenous village Gurah, in a rural area of Mohali district in Punjab state using a demographic and facility survey. A survey questionnaire was used to collect information on the village's socio-demographic, human, livestock, and agricultural profiles. The study included 77 households from the village Gurah, with the majority i.e., 71.4 % engaged in agricultural activity and 68.8 % with livestock. Survey results showed that self-reported adherence to any medicine prescribed by doctors was high (92.3 %) and self-medication reported by the respondents was 11 %. Forty-two percent of antibiotic consumption was verified from prescription. The major crops grown in the village were exposed to pesticides, and most dairy and non-dairy products were sold in markets, with consumers unaware of any pesticide or antibiotic exposure. Additionally, villagers were unaware of disease diagnosis and the medicines their livestock consumed. Findings from veterinarians revealed that around 50 % of the livestock was given antibiotics for treatment for mastitis. In our study, 67.9 % of the green fodder for animals was homegrown and pesticide use was reported. The study reported that 81.1 % of the animal feed additives were purchased from the market and farmers might be unaware whether commercially-purchased feed contains antibiotics. The results provide a picture of the current situation and guide further research for the containment of AMR under the One Health approach. Inadequate multi-sectoral and cross-disciplinary efforts to combating AMR in current practice call for prompt coordinated action integral to a "One Health approach."
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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 | 0.001 |
| Science and technology studies | 0.002 | 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".