Programmatic and ethical challenges in the implementation of treatment-as-prevention in the context of HIV and drug-resistant tuberculosis co-infection in sub-Saharan Africa
Bibliographic record
Abstract
There is limited literature on programmatic challenges in the implementation of a treatment-as-prevention (TasP) strategy among human immunodeficiency virus (HIV) and drug-resistant tuberculosis (DR-TB) co-infected individuals in sub-Saharan Africa (SSA). This paper highlights specific programmatic challenges surrounding the implementation of this strategy among HIV and DR-TB co-infected populations in SSA. In SSA, limitations in administrative, human and financial resources and poor health infrastructure, as well as increased duration and complexity of providing long-term treatment for HIV individuals co-infected with DR-TB, pose substantial challenges to the implementation of a TasP strategy and warrant further investigation. A comprehensive approach must be devised to implement TasP strategy, with special attention paid to the sizable HIV and DR-TB co-infected populations. We suggest that evidence-informed and human rights-based guidelines for participant protection and strategies for programme delivery must be developed and tailored to maximise the benefits to those most at risk of developing HIV and DR-TB co-infection. Assessing regional circumstances is crucial, and TasP programmes in the region should be complemented by combined prevention strategies to achieve the intended goals.
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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 | 0.131 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| 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".