Facing Ethical Challenges in Rolling Out Antiretroviral Treatment in Resource-Poor Countries: Comment on “They Call It ‘Patient Selection’ in Khayelitsha”
Bibliographic record
Abstract
It is widely acknowledged that the HIV and AIDS pandemic is a global emergency and that cheap, effective treatment should be provided for as many people as possible worldwide. But there are many challenges to rolling out antiretroviral (ARV) treatment in resource-poor settings. These include the cost of drugs (although these are falling rapidly), sustaining their supply and distribution, the complexity of treatment regimens, selection of patients for treatment, shortage of medical and nursing personnel, inadequacy of healthcare facilities, the need for uninterrupted, lifelong treatment, and monitoring for drug resistance. Great efforts, nationally and internationally, are required to meet these challenges.This work was supported in part by the University of Toronto and a grant from the United States National Institutes of Health's Fogarty International Center to the University of Cape Town's capacity-building program in International Research Ethics in southern Africa (Program Director S. R. Benatar).
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.069 | 0.080 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".