Achieving Systemic and Scalable Private Sector Engagement in Tuberculosis Care and Prevention in Asia
Bibliographic record
Abstract
Health-Seeking BehaviorsAs a transmissible, airborne disease, tuberculosis (TB) is a classic public health issue, and the majority of TB prevention and care efforts globally have focused on the public-sector role.However, in sub-Saharan Africa and South Asia, respectively, 49% and 81% of all patients present initially to private or informal (nonqualified) providers [1].Many of those patients have TB symptoms and a subset have TB disease, but studies show substantial diagnostic delays [2] and high patient costs [3] that are associated with seeing multiple private providers.Shortening the pathway for those individuals-from their initial private-sector consultation to quality-assured, evidence-based treatment in either the public or private sector-is no Summary Points• Tuberculosis (TB) is a major public health threat.But worldwide, the majority of people with symptoms consistent with TB start their care seeking in the private or informal sector.These numbers are particularly high in Asia.• Public-private mix (PPM) efforts have been introduced to reach these individuals, as soon as possible, with quality-assured diagnosis and treatment.Systematic approaches have been designed to reach all provider types.However, PPM schemes struggle to manage the scale of a fragmented and under-regulated private sector.• Opportunities are arising to introduce more systemic, scalable, and innovative approaches, including social businesses, insurance-based initiatives, intermediary agencies, regulatory regimes, and provider consolidation, with a heavy emphasis on the use of new information technologies.• These approaches combine the previous work on TB private sector engagement with structural solutions that make health systems function for all patients, regardless of the disease or whether patients seek care in the public or the private sector.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".