ANIMAL AND HUMAN HEALTH AND ANTIMICROBIAL RESISTANCE
Bibliographic record
Abstract
Antimicrobial resistance (AMR) represents a formidable global health crisis, compelling the adoption of the One Health approach which integrates human, animal, and environmental health sectors. This publication examines the challenges and strategies in combating AMR, emphasizing the application of One Health principles, especially in resource-constrained settings. The paper outlines significant lessons learned from successful AMR interventions worldwide. Materials and methods: The methodology involves a literature review and analysis of scientific articles available in global databases dedicated to the issue of AMR, as well as materials related to the implementation of various strategies to combat AMR at the national and governmental levels. Results: Challenges identified relate to gaps in surveillance, education, international cooperation, and resource allocation in low-resource settings. The analysis further delves into future research implications, advocating for a deeper understanding of resistance mechanisms, the impact of interventions, the development of rapid diagnostics, vaccine research, and the exploration of alternative therapies. Examples from the European Union, Bangladesh, Canada, China, India, and the USA have been examined. Conclusions: The paper concludes by calling for intensified collaborative efforts to mitigate AMR and tailored strategies that recognize the diverse contexts of countries grappling with resource limitations. Suggesting that addressing AMR requires concerted global efforts, the publication outlines actionable strategies within the One Health framework to maintain the effectiveness of antimicrobials. The future direction emphasizes the significance of comprehensive surveillance, the impact evaluation of interventions, policy development, and community engagement in ensuring sustainable solutions to AMR.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".