Government policy interventions to reduce veterinary antimicrobial consumption in production animals: a protocol for a systematic review and evidence map
Bibliographic record
Abstract
Abstract Background Globally, agricultural production systems consume two-thirds of all antimicrobials. These systems are used to raise animals that produce products for consumption, such as meat, eggs, milk, and wool. The World Bank estimates that by 2030, AMR will reduce global livestock production by up to 7.5%, resulting in economic losses of up to one trillion USD. Governments worldwide have implemented various policies to promote antimicrobial stewardship in production animals, such as requiring veterinary prescriptions for antimicrobial use, restricting certain antimicrobials, and prohibiting antimicrobial use for growth promotion. However, the efficacy of these measures remains uncertain, necessitating a comprehensive review to guide policymakers. This review will identify and describe implemented government policy interventions to reduce veterinary AMU and AMR in production animals. A secondary analysis will map the policy pathways and the stakeholders involved in their successful implementation. Methods An electronic search strategy has been developed in consultation with a public health librarian and a veterinary health librarian. CAB Abstracts, MEDLINE, Web of Science, and ProQuest Dissertations will be searched, and additional studies will be identified using gray literature searches. The intervention of interest is any policy intervention enacted by a government or government agency in any country to change antimicrobial use in production animals. For inclusion within the review, studies must (1) describe the government policy, (2) quantitatively measure the impact of the policy in production animals using a rigorous study design, and (3) measure the impact of the intervention through antimicrobial use (AMU) or AMR. Two independent reviewers will screen for eligibility using defined criteria, and data will be extracted using Covidence software and Excel, respectively. Results will be synthesized narratively and visually (using maps and Sankey plots) to identify evidence gaps. Discussion This systematic review is intended to inform future government policies addressing antimicrobial resistance and antimicrobial use in production animal systems. It will also inform future research priorities by identifying evidence gaps about the effectiveness of various policy interventions. Systematic review registration Open Science framework.
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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 | 0.120 | 0.136 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.087 | 0.013 |
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".