Preparing for Antimicrobial Resistance: Vision and Social Science Mission of the INAMRSS Network
Bibliographic record
Abstract
The COVID-19 pandemic has raised awareness of the urgent need to improve the design of health systems, as well as the practical implementation of new strategies and technical solutions to better prepare for future pandemics. These preparations must also consider harms secondary to the pandemic, including the resulting effects on antimicrobial resistance (AMR). While drug-resistant infections pose a well-known and severe threat to human and animal health, the COVID-19 pandemic is compounding this already problematic situation. Recent regulatory interventions bring hope that we will not be as unprepared in facing this threat. Moreover, public and private initiatives promoting the development of new antimicrobial treatments, such as the recent AMR Action Fund, will most likely provide a few years of breathing room for innovation to ensure there is a path for new antimicrobials to be developed and delivered to patients in need. But, as important as they are, they will only partly compensate for the unresolved, fundamental problems. Moreover, and most importantly, such initiatives do not change the underlying social, cultural, and economic causes and challenges of antimicrobial resistance on a more sustainable basis.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.018 |
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".