Requirements and considerations for effective implementation of integrated One Health antimicrobial resistance research
Bibliographic record
Abstract
The One Health (OH) approach recognizes the interconnectedness of the health of people, animals, plants/crops and ecosystems, and is central to addressing antimicrobial resistance (AMR). The 7th Environmental Dimension of Antimicrobial Resistance Conference (EDAR7), held in Montreal in May 2024, exemplified this approach by convening international experts and stakeholders to discuss AMR research and policy progress. EDAR7 workshop #8 focused on (1) barriers to establishing effective OH AMR research programs, (2) gaps in OH AMR research priorities, and (3) potential solutions/approaches or "tools" to ensure programs develop in accordance with OH principles and generate insightful data that maximizes limited resources. Key workshop outcomes included identifying critical principles for OH AMR research programs and highlighting the pivotal role of sustainable data management strategies. Additionally, the importance of considering AMR policy and risk assessment needs when planning and designing research was emphasized. Discussions explored specific tools and approaches that support the standardized and harmonized collection and analysis of data, and associated challenges of integrating genomics data into current risk assessments and models. Synthesis of the workshop's discussions outlined critical considerations that interdisciplinary OH AMR research programs and networks should prioritize to enhance the impact of their outputs.
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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 | 0.189 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".