Strengthening antimicrobial resistance governance in Europe: a coordinated one health approach
Bibliographic record
Abstract
Antimicrobial resistance (AMR) causes over 35,000 deaths annually in the EU/EEA and is projected to result in 1.91 million global deaths each year by 2050. The second Joint Action on Antimicrobial Resistance and Healthcare-Associated Infections (EU-JAMRAI-2), uniting 128 partners from 30 countries, represents a coordinated EU/EEA effort to curb AMR through a One Health approach. Although nearly all EU/EEA countries have an AMR National Action Plan, our initial assessment revealed that implementation remains constrained by limited resources, weak intersectoral coordination and fragmented leadership. EU-JAMRAI-2 addresses these challenges by promoting harmonized surveillance, strengthening infection prevention and control, applying behaviorally tailored stewardship interventions, ensuring sustainable access to essential antibiotics, and raising awareness among priority target audiences. By analyzing policy gaps and operational barriers, this paper underscores the need for stronger accountability and political commitment to translate strategies into sustainable action. Strengthening AMR governance through a unified European approach is essential to achieve effective, lasting progress against this silent pandemic.
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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 | 0.056 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".