Antimicrobial Resistance in South East Asia: A Participatory Systems Modelling Approach
Bibliographic record
Abstract
Purpose: Our study purpose was to identify (1) the underlying causal system of factors influencing antimicrobial resistance (AMR) development and spread in South East Asia (SEA) and (2) places to intervene by integrating diverse perspectives to find contextspecific solutions.Methods & Materials: Using a complex adaptive systems lens and participatory, qualitative, systems modelling approach, we conducted 2 participatory workshops and 2 interviews involving AMR experts and other disciplinary experts to brainstorm factors influencing AMR and identify leverage points for intervention.Transcripts were thematically analyzed for factors, connections, and leverage points for interventions, which were then transcribed into a causal loop diagram (CLD) using Vensim 8.0.4 and validated via participant feedback.Results: Seventeen participants representing diverse perspectives across the One Health spectrum (e.g., animal welfare, pharmaceutical industry, food industry, water and sanitation, pest control) constructed a CLD that identified 98 factors, interlinked by 393 arrows, that influenced AMR in SEA.Seven themes explained the AMR dynamics illustrated in the CLD: consumer demand; agricultural food production systems; antimicrobial and pesticide/chemical misuse and AMR spread in the environment; inequitable access to quality antibiotics and health care; poor food safety practices; poor knowledge; and a need for research and innovation.Eight 'overarching factors', not included in the CLD because they impact the entire AMR system, emerged as underpinning the AMR dynamics described in each theme: leadership priorities and investments (e.g., privatized health care); poor regulations and enforcement; social and cultural norms; infectious disease prevalence; the drive to survive (e.g., due to food insecurity, poverty); increasing wealth and urbanization; climate change; and the underlying goal of economic prosperity that drives system behaviour.Fifteen leverage points representing different 'overarching' and CLD factors were identified as places to intervene with potential to change AMU and AMR directly (e.g., via setting AMU standards) or indirectly (e.g., via improving food security) in SEA.Conclusion: Our study illustrates AMR as the product of actions across the One Health spectrum and identifies the need for multipronged and multi-level interventions, including actions relevant to achieving the sustainable development goals, to transform our reliance on AMU and mitigate AMR sustainably.
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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 | 0.026 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| 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".