The Landscape of Anthrax Prevention and Control: Stakeholders’ Perceptive in Odisha, India
Bibliographic record
Abstract
The prevalence and outbreaks of anthrax are interlinked with the animal-environment-human context, which signifies the need for collaborative, trans-disciplinary and multi-sectoral approaches for the prevention and control of anthrax. In India, there are hardly any shreds of evidence on the role of various stakeholders' on anthrax prevention and control. Therefore, this study addressed the experiences of various stakeholders on anthrax prevention and control strategies in Odisha, India. A qualitative explorative study was carried out using 42 in-depth-interviews among the stakeholders from health, veterinary and general administrative departments from the block, district, and state level. Two major themes emerged: (1) Epidemiological investigation of anthrax in Odisha, India, and (2) Biological and social prevention strategies for anthrax in Odisha, India. The study emphasizes setting up the surveillance system as per standard guideline, and strengthening the diagnostic facility at a regional medical college laboratory to avoid delay. Moreover, it emphasizes step-up inter-sectoral co-ordination, collaboration and sensitization among health, veterinary, forestry, education, nutrition and tribal welfare departments at all levels in order to reduce the prevalence and control the outbreaks of anthrax in Odisha state. It also recommends raising community literacy, in particular on safe carcass disposal, changing behavior on dead-livestock consumption, and compliance with livestock vaccinations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".