Development of a Video-Observed Therapy System to Improve Monitoring of Tuberculosis Treatment in Thailand: Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Directly observed therapy programs for monitoring tuberculosis (TB) treatment in Thailand are unsustainable, especially during the COVID-19 pandemic. The current video-observed therapy (VOT) system, the Thai VOT (TH VOT), was developed to replace the directly observed therapy program. OBJECTIVE: This study aimed to describe the VOT system design and identify the potential for system improvements. METHODS: This pilot study was conducted in Na Yong district, a small district in Trang province, south of Thailand. The TH VOT system consists of a smartphone app for patients, a secured web-based platform for staff, items used, and standard operating procedures. There were three groups of users: observers who were TB staff, healthy volunteers as simulated patients, and patients with active TB. All participants were trained to follow the standard operating procedures. After 2-week usage, VOT session records were analyzed to measure the compliance of the patients and observers. The User Experience Questionnaire was used to lead the participant users to focus on 6 standard dimensions of usability, and was supplemented with an in-depth interview to identify potential system improvements from users' experience. RESULTS: Only 2 of 16 patients with currently active TB had a usable smartphone. Sixty of 70 drug-taking sessions among 2 patients and 3 simulated patients in 2 weeks were recorded and uploaded. Only 37 sessions were inspected by the observers within 24 hours. All participants needed a proper notification system. An audit system was also requested. CONCLUSIONS: Before upscaling, the cost of smartphone lending, audit management, and notification systems should be elucidated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".