Facility-based directly observed therapy (DOT) for tuberculosis during COVID-19: A community perspective
Bibliographic record
Abstract
Facility-based directly observed therapy (DOT) has been the standard for treating people with TB since the early 1990s. As the commitment to promote a people-centred model of care for TB grows, the use of facility-based DOT has been questioned as issues of freedom, privacy, and human rights have been raised. The disruptions caused by the COVID-19 pandemic and ensuing lockdown measures have fast-tracked the need to find alternative methods to provide treatment to people with TB. In this study, we present quantitative and qualitative findings from a global community-based survey on the challenges of administering facility-based DOT during a pandemic as well as potential alternatives. Our results found that decreased access to transportation, the fear of COVID-19, stigmatization due to overlapping symptoms, and punitive measures against quarantine violations have made it difficult for persons with TB to receive treatment at facilities, particularly in low-resource settings. Potential replacements included greater focus on community-based DOT, home delivery of treatment, multi-month dispensing, and video DOT strategies. Our study highlights the need for TB programs to re-evaluate their approach to providing treatment to people with TB, and that these changes must be made in consultation with people affected by TB and TB survivors to provide a true people-centred model of care.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".