Understanding what citizens think about Antimicrobial Resistance: Deliberative Polling® in six middle-income countries
Bibliographic record
Abstract
Background The pandemic of antimicrobial resistance (AMR) will only be mitigated by policy action and innovation and importantly, supported by local and community action. Last year (2024) with the United Nations General Assembly high level meeting on AMR in September we decided to ascertain citizens’ understanding of the issues and prioritisation for action Methods Over the summer, while intergovernmental negotiations on the outcome document were taking place, we used Deliberative Polling ® , a methodology founded on deliberative democratic theory, in six middle income countries across three continents to explore people's understanding and support for 45 policies that were likely to feature in the political declaration. Results In total 2419 participants were randomised to deliberation intervention (written and video information, facilitated online small group discussions, and expert plenary sessions) or control groups who only completed the pre- and post- deliberation surveys. Support increased significantly through deliberation for 3/4 of the proposals (>90% for 2/3), as well as on knowledge about AMR and internal political efficacy. Proposals relating to infection prevention were most heavily supported across all six countries. We found regional variation in support for proposals relating to informal antibiotic access and the use of antibiotics in food production, with less support for selected proposals from South America Conclusions Deliberative polling is a powerful method of large scale community engagement and this is new for AMR helping us to understand the views of the public relating to policies that will require their support to enact.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".