Evidence for action: a One Health learning platform on interventions to tackle antimicrobial resistance
Bibliographic record
Abstract
Improving evidence for action is crucial to tackle antimicrobial resistance. The number of interventions for antimicrobial resistance is increasing but current research has major limitations in terms of efforts, methods, scope, quality, and reporting. Moving the agenda forwards requires an improved understanding of the diversity of interventions, their feasibility and cost-benefit, the implementation factors that shape and underpin their effectiveness, and the ways in which individual interventions might interact synergistically or antagonistically to influence actions against antimicrobial resistance in different contexts. Within the efforts to strengthen the global governance of antimicrobial resistance, we advocate for the creation of an international One Health platform for online learning. The platform will synthesise the evidence for actions on antimicrobial resistance into a fully accessible database; generate new scientific insights into the design, implementation, evaluation, and reporting of the broad range of interventions relevant to addressing antimicrobial resistance; and ultimately contribute to the goal of building societal resilience to this central challenge of the 21st century.
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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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".