Stewardship according to context: Justifications for coercive antimicrobial stewardship policies in agriculture and their limitations
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is an urgent, global threat to public health. The development and implementation of effective measures to address AMR is vitally important but presents important ethical questions. This is a policy area requiring further sustained attention to ensure that policies proposed in National Action Plans on AMR are ethically acceptable and preferable to alternatives that might be fairer or more effective, for instance. By ethically analysing case studies of coercive actions to address AMR across countries, we can better inform policy in a context-specific manner. In this article, I consider an example of coercive antimicrobial stewardship policy in Canada, namely restrictions on livestock farmers' access to certain antibiotics for animal use without a vet's prescription. I introduce and analyse two ethical arguments that might plausibly justify coercive action in this case: the harm principle and a duty of collective easy rescue. In addition, I consider the factors that might generally limit the application of those ethical concepts, such as challenges in establishing causation or evidencing the scale of the harm to be averted. I also consider specifics of the Canadian context in contrast to the UK and Botswana as example settings, to demonstrate how context-specific factors might mean a coercive policy that is ethically justified in one country is not so in another.
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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 | 0.045 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.082 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".