TB and COVID – Public and private health sectors adapt to a new reality
Bibliographic record
Abstract
Health systems across the world have been baffled by the COVID19 pandemic. Tuberculosis (TB) care and prevention especially in high burden countries has faced disruption to their routine services. Though these setbacks were predicted by many modelling studies, reports and surveys from the field convey the hard reality faced by the TB services. However, health systems have not given up and have become resilient by adapting interesting strategies to overcome these obstacles. The private health sector has also stepped up to the occasion by supporting national TB programs through innovative approaches. The scientific community has laid down several evidence-based recommendations to help TB programs get back on track. Its time to unite these forces to not just overcome the challenge posed by the pandemic but also to build a more resilient health system.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".