ORIGINAL ARTICLE Evaluation of community-based interventions to improve
Bibliographic record
Abstract
Enlisting traditional healers and pharmacists to improve TB detection contributed 38 % to 70 % of new smear-positive case notifications per quarter in a rural district of Tanzania. In Tanzania, people with tuberculosis (TB) commonly self-medicate or visit traditional healers before seeking formal medical care. Between 2009 and 2011, we piloted a community-based project in Kisarawe District to improve TB case notification. The project trained 15 traditional healers and 15 pharmacists to identify and refer individuals with TB symptoms to diagnostic facilities. In addition, the project trained 2 community members to collect and fix sputum from symptomatic individuals onto slides, which they then delivered by bicycle to the nearest diagnostic facility. To determine effectiveness, we analyzed routine case detection data and referrals from traditional healers and pharmacists and conducted a cross-sectional survey of recently diagnosed smear-positive TB patients (N5150) to understand their treatment-seeking behavior. From 2009 to 2011, smear-positive TB case notification increased by 68 % in Kisarawe District, from 28/100,000 to 47/100,000, even while TB case notification nationally stayed the same (at approximately 14/100,000). The traditional healers and pharmacists referred 434 people with presumptive TB to diagnostic facilities, 419 of whom (97%) went to the facilities; of those who went to facilities for testing, 104 people (25%) were diagnosed with TB. The percentage of new TB case notifications that were referred through the network ranged from 38 % to 70%
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".