Allocation of antiretroviral drugs to HIV-infected patients in Togo: perspectives of people living with HIV and healthcare providers
Bibliographic record
Abstract
AIM: To explore the way people living with HIV and healthcare providers in Togo judge the priority of HIV-infected patients regarding the allocation of antiretroviral drugs. METHOD: From June to September 2015, 200 adults living with HIV and 121 healthcare providers living in Togo were recruited for the study. They were presented with stories of a few lines depicting the situation of an HIV-infected patient and were instructed to judge the extent to which the patient should be given priority for antiretroviral drugs. The stories were composed by systematically varying the levels of four factors: (a) the severity of HIV infection, (b) the financial situation of the patient, (c) the patient's family responsibilities and (d) the time elapsed since the first consultation. RESULTS: Five clusters were identified: 65% of the participants expressed the view that patients who are poor and severely sick should be treated as a priority, 13% prioritised treatment of patients who are poor and parents of small children, 12% expressed the view that the poor should be treated as a priority, 4% preferred that the sickest be treated as a priority and 6% wanted all patients to get treatment. CONCLUSIONS: WHO's guideline regarding antiretroviral therapy allocation (the sickest first as the sole criterion) currently in use in many African countries does not reflect the preferences of Togolese people living with HIV. For most HIV-infected patients in Togo, patients who cannot get treatment on their own should be treated as a priority.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".