Preferential adherence to antiretroviral therapy over tuberculosis treatment: A qualitative study of drug-resistant TB/HIV co-infected patients in South Africa
Bibliographic record
Abstract
Adherence to antiretroviral therapy (ART) and second-line antituberculosis medications is essential to achieve successful outcomes among individuals co-infected with HIV and multi or extensively drug-resistant TB (M/XDR-TB). In 2012-2013, we designed a qualitative study to explore barriers to adherence in KwaZulu-Natal, South Africa. We conducted six focus groups comprising 23 adults receiving treatment for either MDR-TB (n = 2) or XDR-TB (n = 21); 17 were on concurrent ART. Participants expressed a preference for ART over M/XDR-TB treatment as a result of greater tolerability, lower pill burden and a commitment to ART. Treatment outcomes and the social morbidity associated with M/XDR-TB, characterised by public notification, stigma and social isolation, were perceived to be worse than with HIV. Poor communication, low patient involvement and provider supervision of treatment exacerbated participants' negative experiences with TB care. To improve adherence, it is critical that new regimens for drug-resistant TB be developed with better efficacy, lower pill burden and fewer adverse effects. For the first time, such improved regimens are on the horizon. In parallel and equally important is the implementation of a cohesive approach that promotes patient involvement, empowerment and treatment literacy for HIV and for TB.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".