Quality of tuberculosis care in the private health sector
Bibliographic record
Abstract
As countries move towards achieving universal health coverage, efforts to engage all care providers have gained more significance. Over a third of people estimated to have developed TB in 2018 were not detected and notified by national TB programs (NTPs). This gap is more pronounced in countries with large private sectors, especially those with a high burden of TB. Health care providers outside the scope of NTPs, including the private and informal sector, are often the first point of care for TB patients. However, these providers are not fully engaged despite evidence from country experiences and projects that demonstrate increased detection and good treatment outcomes through publicprivate mix (PPM) approaches. While there are often concerns about quality of care in public facilities, there is also increasing evidence that quality of TB care in the private sector falls short of international standards in many places and urgently needs improvement. Failure to engage the full range of health care providers for TB has serious consequences in terms of access to quality care, resulting in increased transmission as a result of delayed diagnosis and treatment; excess mortality and morbidity as a result of inappropriate treatment; and increased drug resistance as a result of incomplete treatment. Recent attention to this issue has led to significant increases in private TB notifications, especially in India, Indonesia and the Philippines, but the gap between notification and the extension of quality program services for provision of treatment and care appears to be growing.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".