Governing Antimicrobial Resistance (AMR) in a Changing Climate: A Participatory Scenario Planning Approach Applied to Sweden in 2050
Bibliographic record
Abstract
Background Antimicrobial resistance (AMR) is a growing global crisis with long-term and unpredictable health, social and economic impacts, with which climate change is likely to interact. Understanding how to govern AMR amidst evolving climatic changes is critical. Scenario planning offers a suitable approach. By envisioning alternative futures, stakeholders more effectively can identify consequences, anticipate problems, and better determine how to intervene. This study explored future worlds and actions that may successfully address AMR in a changing climate in a high-income country, using Sweden as the case. Methods We conducted online scenario-building workshops and interviews with eight experts who explored: (1) how promising interventions (taxation of antimicrobials at point of sale, and infection prevention measures) could each combat AMR in 2050 in Sweden given our changing climate; and (2) actions to take starting in 2030 to ensure success in 2050. Transcripts were thematically analyzed to produce a narrative of participant validated alternative futures. Results Recognizing AMR to be a global problem requiring global solutions, participants looked beyond Sweden to construct three alternative futures: (1) “Tax Burn Out” revealed taxation of antimicrobials as a low-impact intervention that creates inequities and thus would fail to address AMR without other interventions, such as infection prevention measures. (2) “Addressing the Basics” identified infection prevention measures as highly impactful at containing AMR in 2050 because they would contribute to achieving the Sustainable Development Goals (SDGs), which would be essential to tackling inequities underpinning AMR and climate change, and help to stabilize climate-induced mass migration and conflicts; and (3) ”Siloed Nations” described a movement toward nationalism and protectionism that would derail the “Addressing the Basics” scenario, threatening health and wellbeing of all. Several urgent actions were identified to combat AMR long-term regardless which future un-folds, such as global collaboration, and a holistic approach where AMR and climate change are addressed as interlinked issues. Conclusion Our participatory scenario planning approach enabled participants from different sectors to create shared future visions and identify urgent actions to take that hinge on global collaboration, addressing AMR and climate change together, and achieving the SDGs to combat AMR under a changing climate.
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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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".