Should the Increased Awareness of the One Health Approach Brought by the COVID-19 Pandemic Be Used to Further Tackle the Challenge of Antimicrobial Resistance?
Bibliographic record
Abstract
Several experts have expressed their concerns regarding the potential increase in antimicrobial resistance (AMR) during the COVID-19 pandemic as a consequence of the increase in antimicrobial and biocide use in humans globally. However, the impact of the pandemic on antimicrobial use (AMU) and AMR in animals has yet to be discussed and evaluated. Indeed, veterinary practices have been hugely impacted by the pandemic and its restrictive measures around the world. In this perspective, we call for more research to estimate the impact of COVID-19 on AMU and AMR in both humans and animals, as well as on the environment, in coherence with the One Health approach. In addition, we argue that the current pandemic is an opportunity to accelerate the implementation of a One Health approach to tackle the AMR crisis at the global scale. Indeed, the momentum created by the increased general awareness of both the public and decision-makers for the development and maintenance of effective drugs to treat human infections, as well as for the importance of a One Health approach to prevent the emergence of infectious diseases, should be used as a lever to implement global collaborative and sustainable solutions to the complex challenges of 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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".