Health systems appraisal of the response to antimicrobial resistance in low‐ and middle‐income countries in relation to <scp>COVID</scp>‐19: Application of the <scp>WHO</scp> building blocks
Bibliographic record
Abstract
COVID-19 has inflicted both beneficial and damaging effects on health systems responding to antimicrobial resistance (AMR). Data shows that the positive impacts of the pandemic (including enhanced hygiene, mask wearing and widespread use of personal protective equipment), are likely to have been overshadowed by the negative effects: emerging AMR pathogens and mechanisms; further outbreaks and geographic spread of AMR to non-endemic countries; rising infections from multidrug-resistant pathogen; an overall higher burden of AMR. The multisectoral complexities of AMR and the totality of health systems challenge our ability to understand the impact of the COVID-19 pandemic on country responses to AMR. In this analysis, we synthesise international evidence characterising the role of the pandemic on the six key building blocks of health systems in responding to AMR across low- and middle-income countries (LMICs). We apply systems thinking within and between the building blocks to contextualise the impact of one pandemic on another.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".