Unlocking the potential of informal healthcare providers in tuberculosis care: insights from India
Bibliographic record
Abstract
In 2022, tuberculosis (TB) remained a major global health concern, second only to COVID-19 in mortality from a single infectious agent. Over 10 million people contract TB annually, with two-thirds of cases from eight high-burden countries. India alone accounted for 27% of the global burden, totalling an estimated 2.8 million cases.1 Notably, approximately 18% of these people were considered ‘missing’, either undiagnosed or not reported, because they were likely managed by the private sector, which serves the healthcare needs of about half of the patients with TB in the country. The private health sector in India, which delivers approximately 87% (in some regions, particularly if underserved) of initial primary care, is diverse and largely unregulated, extending from small clinics to multispecialty hospitals and ranging from informal providers to highly qualified specialists.2 This poses significant challenges, as patients seeking care from this sector often experience delayed TB diagnoses and inappropriate treatments.3 Therefore, to enhance TB care access and quality, it is essential to involve all healthcare providers in the private sector, both formal and informal, within the framework of the Public-Private Mix, as recommended by India’s National Strategic Plan (NSP) for TB elimination (2017–2025).
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".