Poultry slaughter and carcass disposal practices in Bangladesh: Piloting the use of killing cones to reduce avian influenza transmission
Bibliographic record
Abstract
Abstract Mobile poultry vending and slaughtering of sick poultry have been linked to the spread of highly pathogenic avian influenza (HPAI) H5N1 in Bangladesh. However, limited data exist on associated practices and potential interventions to improve biosecurity among mobile poultry vendors. This mixed-method study was conducted in three phases across four sub-districts in Bangladesh. In phase 1, researchers conducted 416 hours of structured observation, 40 in-depth interviews with poultry vendors, and 40 informal interviews with the customers. Phase 2 involved the development and pilot testing of an intervention package, which included poultry slaughtering cones, hand sanitizers, disinfectants, and a hygiene pamphlet, with 10 vendors. Phase 3 implemented the full intervention with 20 vendors, followed by 94 hours of observation and 17 customer interviews. At baseline, vendors sourced poultry from multiple locations, kept them in small cages on rickshaw vans, and slaughtered them in open spaces, drains, or near water sources. Waste was often discarded in the environment or fed to animals. Vendors demonstrated limited hygiene knowledge and were not observed using personal protective equipment, soap, or disinfectants. Post-intervention, vendors adopted improved practices such as using killing cones, containing waste, disinfecting the slaughtering area, and cleaning hands before eating. Customers also viewed the intervention positively. The study highlights significant risks of AIV transmission through mobile poultry vending but demonstrates that low-cost, targeted interventions can enhance hygiene and biosecurity. Further research is needed to assess long-term sustainability and scalability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".