Patient and Provider Reported Reasons for Lost to Follow Up in MDRTB Treatment: A Qualitative Study from a Drug Resistant TB Centre in India
Bibliographic record
Abstract
INTRODUCTION: Multidrug-resistant Tuberculosis (MDR TB) is emerging public health concern globally. Lost to follow-up (LTFU) is one of the key challenge in MDRTB treatment. In 2013, 18% of MDR TB patients were reported LTFU in India. A qualitative study was conducted to obtain better understanding of both patient and provider related factors for LTFU among MDR TB treatment. METHODS: Qualitative semi-structured personal interviews were conducted with 20 MDRTB patients reported as LTFU and 10 treatment providers in seven districts linked to Nagpur Drug resistant TB Centre (DRTBC) during August 2012-February 2013. Interviews were transcribed and inductive content analysis was performed to derive emergent themes. RESULTS: We found multiple factors influencing MDR TB treatment adherence. Barriers to treatment adherence included drug side effects, a perceived lack of provider support, patient financial constraints, conflicts with the timing of treatment services, alcoholism and social stigma. CONCLUSIONS: Patient adherence to treatment is multi-factorial and involves individual patient factors, provider factors, and community factors. Addressing issue of LTFU during MDRTB treatment requires enhanced efforts towards resolving medical problems like adverse drug effects, developing short duration treatment regimens, reducing pill burden, motivational counselling, flexible timings for DOT services, social, family support for patients & improving awareness about disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".