Antimicrobial resistance management in Pacific Island countries: Current status, challenges, and strategic solutions
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is currently recognized as a major emerging threat to human and animal health. The burden of antimicrobial-resistant infections affects the economy in developed and developing countries. There is a rapid rise in AMR in human and veterinary medicine globally. AMR profiles are poorly documented in Fiji, and limited data are accessible. Fiji currently has no national veterinary antibiotic resistance surveillance network or regulations and guidelines on veterinary drug use. However, available literature shows that although human drugs are better managed than veterinary drugs, the knowledge is still constrained and dispersed. Furthermore, Fiji was chosen as a case study to develop a prototype AMR surveillance and control in the Pacific region. Pacific Island countries share similar geographic and climatic conditions. Currently, the Australian Centre for International Agricultural Research has funded an AMR project that addresses some gaps in managing AMR in the region. The project is the first to adopt the One Health approach to research the AMR in humans, animals, and the Pacific region's environment. Combating AMR needs human health and veterinary personnel to work with all other stakeholders. Continuous surveillance for resistant clinical isolates in humans and animals and the development of appropriate policy intervention measures in human and veterinary drug use are necessary to alleviate AMR burden. Therefore, there is a need to educate farmers, human patients, and the public on the fight against AMR. In addition, AMR data are necessary to develop effective AMR control strategies. This review gives a comprehensive information assessment on AMR in Fiji and the other South Pacific Islands in relation to global trends. Suggestions on the most appropriate ways of effectively managing AMR in Fiji have been made.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".