Taking stock of global commitments on antimicrobial resistance
Bibliographic record
Abstract
Over the last six years, antimicrobial resistance (AMR) has generated an unprecedented amount of global attention. This global attention has coincided with an increase in discussion around AMR at various multilateral organisations and international fora. This study catalogues and analyses AMR-related commitments made by the global community following the implementation of the AMR Tripartite's Global Action Plan (GAP) in 2015. In examining these commitments, we elucidated emergent themes and gaps in AMR discourse through a qualitative content analysis of global political resolutions, declarations and statements made by members of the United Nations, the World Health Assembly, Food and Agriculture Organization Conferences, World Organisation for Animal Health General Sessions, and the G7 and G20 summits and ministerial meetings between the years 2015 and 2021. Emergent themes included AMR research, surveillance and stewardship. Across sectors, fewer commitments were made for specific action on AMR in the environment. The themes and types of commitments were found to be consistent across time and fora but did not evolve into more concrete or nuanced pledges to action between 2015 and 2021. GAP objectives relating to infection prevention and efforts to address the root drivers of AMR appeared the least frequently in our analysis, indicating a lack of global commitment to take a proactive prevention-focused approach to AMR.
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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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".