Integrating patients' perspectives into integrated tuberculosis-human immunodeficiency virus health care
Bibliographic record
Abstract
BACKGROUND: Escalating rates of tuberculosis-human immunodeficiency virus (TB-HIV) co-infection call for improved coordination of TB and HIV health care services in high-burden countries such as South Africa. Patient perspectives, however, are poorly understood in the context of current integration efforts. METHOD: Under a qualitative research framework, we interviewed 40 HIV-positive adult TB patients and eight key-informant health care workers across three clinics in KwaZulu-Natal Province to explore non-clinical and non-operational aspects of TB-HIV health care. FINDINGS: Qualitative analysis highlighted critical social and ethical considerations for the concurrent delivery of TB and HIV care. Co-infected patients navigating between TB and HIV programs are exposed to missed opportunities for TB and HIV service integration, fragmented or vertical care for their dual infections and contrasting experiences within TB and HIV clinics. These intersecting issues appear to affect patients' health-related decisions, particularly nondisclosure of HIV status to non-HIV health care workers and their preferences for integrated health care. CONCLUSION: Our study highlights the imperative to address service fragmentation, HIV medical confidentiality and provider mistrust within the health care system, and the cultural differences associated with TB and HIV disease control.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".