Stakeholder analysis for ‘One Health’ approach to tackle antimicrobial resistance
Bibliographic record
Abstract
Antimicrobial resistance (AMR) and interventions to mitigate it are multisectoral, exhibiting super-wicked features that require intersectoral collaboration and synergy. Although AMR and mitigation strategies are pressing issues, their solutions are complex, ethically challenging, multilayered and often conflict at various levels and among diverse stakeholders. The main objective of this study was to identify the values and potential contributions of stakeholder analysis related to AMR and potential interventions from a case study that is being undertaken in Nepal using a 'One Health' approach. A total of 33 representatives from human, animal, agricultural and environmental sectors attended a stakeholder meeting in Kathmandu to discuss AMR, its ethical and practical challenges, opportunities and potential interventions. Using a five-pillar framework for stakeholder analysis, we demonstrate its relevance for addressing AMR and propose practical considerations for implementing effective interventions in Nepal. Beyond the practical discussions on AMR and its interventions at the policy, implementation and practice levels, this study underscores the critical value of its methodological reflections for informing ongoing interventions both within Nepal and in similar contexts globally.
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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 | 0.104 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".