Understanding the veterinary antibiotics supply chain to address antimicrobial resistance in Lao PDR: Roles and interactions of involved stakeholders
Bibliographic record
Abstract
In response to the global call to mitigate risks associated with antimicrobial resistance (AMR), new regulations on the access and use of veterinary antibiotics are currently being developed by the Lao government. This study aims to explore how the implementation of these new regulations might effectively reduce and adapt the sale, distribution and use of veterinary antibiotics in Lao PDR. To this end, we used the theory of change, framing the AMR issue within the context of the stakeholders involved in the veterinary antibiotics supply chain. Qualitative and quantitative methods were used to collect data, based on questionnaires (n=36 antibiotic suppliers, n=96 chicken farmers, n=96 pig farmers), and participatory tools such as a workshop (n=10 participants), semi-structured interviews (n=20), and focus group discussions (n=7 participants). The stakeholders' understanding of the AMR issue and potential challenges related to the implementation of new regulations regarding access and use of antibiotics, were also investigated. We mapped the veterinary antibiotic supply chain in Lao PDR, and analysed the roles and interactions of its stakeholders. Twenty-three stakeholders representing the private and the public sectors were identified. Many informal and formal links connected these stakeholder within this supply chain. The lack of veterinarian-farmer interaction and the evolving nature of the veterinary antibiotics supply chain accentuated the challenges of achieving behaviour change through regulations. Most of the antibiotics found on farms were categorized by the World Health Organisation's as critically important antibiotics used in human medicine. We argue that AMR risk mitigation strategy requires dialogue and engagement between private and public sectors stakeholders, involved in the importation, distribution, sale and use of veterinary antibiotics. This study further highlighted that AMR is a complex adaptive challenge requiring multi-sectoral approach. We believed that a sustainable approach to reduce and adapt veterinary antibiotics use should be prepared in collaboration with stakeholders from private and public sectors identified in this study, in addition to the new regulations. This collaboration should start with the co-construction of a common understanding of AMR issue and of the objectives of new regulations.
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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 | 0.009 | 0.007 |
| 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 | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".