Implementing a public-private mix model for tuberculosis treatment in urban Pakistan: lessons and experiences
Bibliographic record
Abstract
SETTING: Six towns of Karachi, Pakistan. OBJECTIVES: 1) To strengthen the capacity of general practitioners (GPs) in providing tuberculosis (TB) treatment through DOTS; and 2) to enhance collaboration between the public and private sectors in TB management and case reporting. DESIGN: A quasi-experimental study design was adopted to ensure enrolment of TB patients through trained GPs with the support of laboratory networks and to improve the case detection rate. RESULTS: The following challenges were faced during implementation of the model in urban settings: no systematic list of GPs was available; the majority of the GPs were untrained health practitioners working in squatter settlements, where formally trained GPs are most needed; the motivation of GPs with high patient loads is very low; and access to a laboratory is difficult. Of 35 patients enrolled in the first quarter (third quarter 2009), 87% completed their treatment successfully. CONCLUSION: Public-private mix (PPM) DOTS is feasible in the cities of Pakistan. However, the cost, time and effort required to establish the programme is higher than in many other developing countries.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".