A study protocol on assessing the acceptance and effectiveness of a digital adherence technology for TB preventive treatment in Bangladesh
Bibliographic record
Abstract
Abstract Background Adherence to tuberculosis preventive treatment (TPT) remains a significant challenge in high-burden countries like Bangladesh, where approximately 44 million people are infected with latent tuberculosis. Methods/Design This study protocol describes a mixed-methods observational study to evaluate “iDOTS,” a locally developed digital adherence technology adapted from 99DOTS, for monitoring and improving TPT adherence among adult household contacts of bacteriologically confirmed pulmonary TB patients. The study will be conducted in two districts in Bangladesh with similar geographical and societal characteristics, with Narsingdi as the intervention site and Manikganj as the control site. Applying the Unified Theory of Acceptance and Use of Technology (UTAUT), we will assess technology acceptance, implementation challenges, and effectiveness through quantitative and qualitative approaches. The quantitative component will compare TPT adherence between iDOTS users and non-users, while qualitative interviews will explore user experiences and attitudes among healthcare providers and patients. Adherence will be verified through a combination of digital records, self-reports, and random isoniazid urine testing. Discussion With an estimated sample size of 422 patients and 77 healthcare providers, this study aims to generate evidence that if digital adherence technologies can strengthen TPT implementation in resource-limited settings. The findings will address critical gaps in the TPT cascade and inform strategies for scaling up TPT nationally, ultimately supporting global efforts to reduce the TB disease burden through effective preventive measures. Trial registration ‘Not applicable’
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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 | 0.055 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.128 | 0.023 |
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".