Antimicrobial Resistance and Environmental Health: A Water Stewardship Framework for Global and National Action
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a global health crisis that affects all life on Earth. In 2015, the World Health Organization developed guidance to combat AMR in accordance with a One Health framework considering human, animal, and environment sectors of planetary health. This study reviewed global guidance and 25 National Action Plans to evaluate thematic priorities in One Health AMR approaches using a novel framework that additionally facilitated the identification of water-related stewardship gaps, as water resources are recognized as the primary environmental AMR reservoir and dissemination pathway. This review found that global and national stewardship primarily focuses on mitigating antibiotic use in the human and animal sectors, overlooking environmental drivers, particularly diverse environmental waters. The findings of this study highlight the need to broaden the scope of water-related AMR concerns beyond water, sanitation, and hygiene (WASH) infrastructure for water supply and wastewater treatment, and account for environmental waters in AMR development and dissemination, particularly in low-income countries where half a billion people rely on environmental waters to meet daily needs. Equitably accounting for water environments, supplies, and waste in AMR prevention, mitigation, surveillance, and innovation can significantly enhance the integration of environmental objectives in One Health AMR stewardship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".