Towards a matrix to assess the impacts of integrated surveillance of antimicrobial resistance and antimicrobial use
Bibliographic record
Abstract
Introduction - Tackling the challenge of antimicrobial resistance (AMR) requires changes in antimicrobial use (AMU) across human, animal and agricultural sectors. To trigger adequate changes, both at individual as at policy level, all actors in the system must be informed by integrated surveillance. Consequently, it is crucial to assess the impacts of the information produced by such integrated systems to provide evidence of their added value. In this context, we aimed at developing a matrix to assess the impacts of integrated surveillance of AMR and AMU. Materials and Methods A preliminary evaluation tool was developed using impact pathway analysis. Using evidence from previous research, evaluation attributes and their indicators were identified for each of the immediate, intermediate and ultimate outcomes. The preliminary tool will be then submitted to a panel of international experts for validation and prioritization according to the surveillance context. Results - The evaluation matrix includes outcomes´ attributes to assess changes in surveillance performance, stakeholder awareness, knowledge and practices, cross-sectoral collaboration, systems knowledge, AMR risk management, surveillance costs, as well as health and economic impacts. Indicators are either observed or stated, and measured using both qualitative (eg. document analysis, key informant interviews) and quantitative methods (eg. epidemiological, economic analysis). The expert opinion elicitation will allow us to identify the adequate evaluation timeline, as well as the appropriateness of the attributes and indicators depending on the maturity of the evaluated system. Conclusion - The developed evaluation matrix provides guidance to assess the impacts of integrated surveillance systems of AMR and AMU conducted in a One Health context. It can be used as a stand-alone tool to specifically assess the outcomes of the surveillance system, or in conjunction with existing surveillance tools to provide a comprehensive evaluation of the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".