“When Treatment Is More Challenging than the Disease”: A Qualitative Study of MDR-TB Patient Retention
Bibliographic record
Abstract
BACKGROUND: One-fifth of the patients on multidrug-resistant tuberculosis treatment at the Drug-Resistant-TB (DR-TB) Site in Gujarat are lost-to-follow-up(LFU). OBJECTIVE: To understand patients' and providers' perspectives on reasons for LFU and their suggestions to improve retention-in-care. DESIGN: Qualitative study conducted between December 2013-March 2014, including in-depth interviews with LFU patients and DOT-providers, and a focus group discussion with DR-TB site supervisors. A thematic-network analysis approach was utilised. RESULTS: Three sub-themes emerged: (i) Struggle with prolonged treatment; (ii) Strive against stigma and toward support; (iii) Divergent perceptions and practices. Daily injections, pill burden, DOT, migratory work, social problems, prior TB treatment, and adverse drugs effects were reported as major barriers to treatment adherence and retention-in-care by patients and providers. Some providers felt that despite their best efforts, LFU patients remain. Patient movements between private practitioners and traditional healers further influenced LFU. CONCLUSION: The study points to a need for repeated patient counselling and education, improved co-ordination between various tiers of providers engaged in DR-TB care, collaboration between the public, private and traditional practitioners, and promotion of social and economic support to help patients adhere to MDR-TB treatment and avoid LFU.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".