Criteria used for selecting patients for antiretroviral therapy in Uganda: A qualitative study
Bibliographic record
Abstract
Limited resources in low income countries still prevent HIV patients from accessing treatment and necessitate the rationing of anti retroviral therapy. This paper aims to describe the criteria used in actual patient selection so as to develop evidence based recommendations for improving fairness in patient selection in Uganda and similar contexts. Qualitative interviews (n = 37) from six AIDS treatment units in Uganda; review of policy and clinic documents; and group discussions (n = 47) people living with AIDS. Practitioners identified both medical criteria (need, CD4 count, WHO staging, Absence of severe co-infections, patient readiness, and ART naivety) and social criteria (economic status, social support, treatment buddy, disclosure, duration with organization, distance, alcohol consumption, relatives of clients on ART, first-in- first- out, vulnerability and activism). There was congruence around the medical criteria across institutions and the national guidelines; and variations around the social criteria. The variations around the social criteria necessitate more explicit debate. Commonly used and accepted criteria could be considered for explicit inclusion in the national guidelines. Disputed criteria should be debated to identify an acceptable set of criteria for ART rationing. These criteria should be publicized to facilitate on-going revisions, ensure consistency, and contribute to fair patient selection. Key words: HIV/AIDS, anti-retroviral treatment, Uganda, rationing, criteria.
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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 | 0.045 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".