Mapping out a One Health model of antimicrobial resistance in the context of the Swedish food system: a literature scan
Bibliographic record
Abstract
Abstract Background: Antimicrobial resistance (AMR) causes worsening health, environmental, and financial burdens. Modelling complex issues such as AMR is important, however, how well such models and data cover the broader One Health system is unknown. Our study aimed to identify models of AMR across the One Health system (objective 1), and data to parameterize such models (objective 2) to inform a future model of the AMR in the Swedish One Health system. Based on an expert-derived qualitative description of the system, an extensive literature scan was performed to identify models and data from peer-reviewed and grey literature sources. Models and data were extracted, categorized in an Excel database, and visually represented on the existing qualitative model to illustrate coverage. The articles identidied described 106 models in various parts of the One Health system; 54 were AMR-specific. Few multi-level, multi-sector models, and models within the animal and environmental sectors, were identified. We identified 414 articles containing data to parameterize the models. Data gaps included the environment and broad, ill-defined, or abstract ideas (e.g., human behaviour). In conclusion, no models addressed the entire system, and many data gaps were found. Existing models could be integrated into a mixed-methods model in the interim.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".