Whose burden, whose benefit? Revisiting ethical trade-offs in the WHO guidelines on scaling up mass azithromycin administration
Bibliographic record
Abstract
New evidence suggests that mass drug administration of azithromycin (MDAA) can significantly reduce childhood mortality in high-burden, low-resource settings, yet the World Health Organization's (WHO) 2020 guidelines take a cautious approach due to concerns about antimicrobial resistance (AMR).While the WHO guidelines cite ethical principles, they insufficiently address key considerations, such as intergenerational justice, equitable burden sharing, and the structural determinants of health that shape infectious disease vulnerability.Global AMR policy often prioritizes conservation over access in ways that disproportionately burden low-income countries, despite high-income countries also bearing significant responsibility for the emergence and spread of AMR.A balanced ethical framework is needed: one that explicitly integrates contextual values, including justice across generations, historical inequities, and community input under uncertainty.Revised WHO guidelines that expand eligibility for MDAA based on context-specific criteria, establish thresholds for mortality and resistance monitoring, and encourage global investment in sustainable health systems and antibiotic access, may better align with the WHO's own principles on equity, human rights, and social determinants of health in the development of guidelines.
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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 | 0.257 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.029 | 0.049 |
| Insufficient payload (model declined to judge) | 0.002 | 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".