Engaging Informal Providers in TB control: What Is the Potential in the Implementation of the WHO Stop TB Strategy? A Discussion Paper
Bibliographic record
Abstract
The World Health Organization (WHO) Stop TB Strategy calls for involvement of all healthcare providers in tuberculosis (TB) control. There is evidence that many people with TB seek care from informal providers before or after diagnosis, but very little has been done to engage these informal providers. Their involvement is often discussed with regard to DOTS (directly observed treatment - short course), rather than to the implementation of the comprehensive Stop TB Strategy. This paper discusses the potential contribution of informal providers to all components of the WHO Stop TB Strategy, including DOTS, programmatic management of multi-drug-resistant TB (MDR-TB), TB/HIV collaborative activities, health systems strengthening, engaging people with TB and their communities, and enabling research.The conclusion is that with increased stewardship by the national TB program (NTP), informal providers might contribute to implementation of the Stop TB Strategy. NTPs need practical guidelines to set up and scale up initiatives, including tools to assess the implications of these initiatives on complex dimensions like health systems strengthening.
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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 | 0.040 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.005 |
| 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".