The social burden of antimicrobial resistance: what is it, how can we measure it, and why does it matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a growing global health threat, which is increasingly quantified in terms of its human health and economic burden. In this article, we highlight that for policy and planning purposes the social burden of AMR is as important to attend to as health and economic burdens, requiring systematic consideration and measurement of multiple dimensions. We provide a conceptual and empirical overview of four dimensions of the social burden of AMR: the distribution of AMR among and between populations; the lived experiences of AMR by patients and carers; how and by whom AMR interventions are shouldered; and how AMR can change society. We illustrate these dimensions through five case studies drawn from research projects in the UK, East Africa, Thailand and Brazil. Drawing on these insights, we discuss challenges and opportunities for documentation and measurement of AMR's social burden going forward. Taking this seriously aligns with the consensus observation that to address AMR requires moving away from pathogen-based and siloed disciplinary perspectives and means embracing different forms of data and evidence from around the world. We propose an interdisciplinary engagement across researchers, policy makers and community stakeholders to arrive at agreed principles and metrics for future monitoring of the social burden. We need to tackle invisibility through lack of data by considering the social burden in design of AMR surveillance and research, includes mainstreaming social science data, and incorporating arts-based approaches to understanding AMR. Recognition, documentation and measurement of the social burdens of AMR will advance AMR approaches and help develop equitable solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 it