Antimicrobial resistance in South East Asia: time to ask the right questions
Bibliographic record
Abstract
Antimicrobial resistance (AMR) has emerged as a major public health concern, around which the international leadership has come together to form strategic partnerships and action plans. The main driving force behind the emergence of AMR is selection pressure created due to consumption of antibiotics. Consumption of antibiotics in human as well as animal sectors are driven by a complex interplay of determinants, many of which are typical to the local settings. Several sensitive and essential realities are tied with antibiotic consumption - food security, livelihoods, poverty alleviation, healthcare access and national economies, to name a few. That makes one-size-fits-all policies, framed with the developed country context in mind, inappropriate for developing countries. Many countries in the South East Asian Region have some policy structures in place to deal with AMR, but most of them lack detailed implementation plans or monitoring structures. In this current debates piece, the authors argue that the principles driving the AMR agenda in the South East Asian countries need to be dealt with using locally relevant policy structures. Strategies, which have successfully reduced the burden of AMR in the developed countries, should be evaluated in the developing country contexts instead of ad hoc implementation. The Global Action Plan on AMR encourages member states to develop locally relevant National Action Plans on AMR. This policy position should be leveraged to develop and deploy locally relevant strategies, which are based on a situation analysis of the local systems, and are likely to meet the needs of the individual member states.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".