A Rapid Review of Environmental Health Gaps in Antimicrobial Resistance and Water-Related Research from 1990–2020
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a pervasive global health threat linked to human antimicrobial misuse and abuse, food production, and broader environmental contamination. While global agencies promote a multi-sectoral One Health system approach to equitably combat human, animal, and environmental health AMR risks, it is widely acknowledged that the human and animal sectors dominate discussions. Given this disproportionate focus, identification of critical research gaps is needed to develop stewardship plans that equitably address One Health AMR threats. This review used natural language processing and term frequency algorithms to classify 12,638 records from 1990-2020 thematically in order to highlight sectoral prioritization and gaps. It also specifically assessed water-related gaps as water is recognized as both a primary environmental dissemination pathway and key means of intervention. Drawing from systemic health and integrated water management lenses, this review found that themes related to plant, wildlife, and environmental-related AMR threats-in particular, the role that environmental (ambient) waters play in AMR development, transmission, and spread-are under-prioritized as compared to human and food animal health concerns regardless of geographic region or income level. Further prioritization of these themes is needed to strengthen the environmental dimension of One Health AMR responses and systemically protect global health.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".