Lessons for TB from the COVID-19 response: qualitative data from Brazil, India and South Africa
Bibliographic record
Abstract
BACKGROUND: Brazil, India and South Africa are among the top 30 high TB burden countries globally and experienced high rates of SARS-CoV-2 infection and mortality. The COVID-19 response in each country was unprecedented and complex, informed by distinct political, economic, social and health systems contexts. While COVID-19 responses have set back TB control efforts, they also hold lessons to inform future TB programming and services. METHODS: = 76) in Brazil, India and South Africa 2 years into the COVID-19 pandemic. Interview transcripts were analysed using an inductive coding strategy. RESULTS: Political will - whether national or subnational - enabled implementation of widespread prevention measures during the COVID-19 response in each country and stimulated mobile and telehealth service delivery innovations. Participants in all three countries emphasised the importance of mobilising and engaging communities in public health responses and noted limited health education and information as barriers to implementing TB control efforts at the community level. CONCLUSIONS: Building political will and social mobilisation must become more central to TB programming. COVID-19 has shown this is possible. A similar level of investment and collaborative effort, if not greater, as that seen during the COVID-19 pandemic is needed for TB through multi-sectoral partnerships.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| 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".