Active Referral: An Innovative Approach to Engaging Traditional Health Providers in TB Control in Burkina Faso
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The involvement of traditional healthcare providers (THPs) has been suggested among strategies to increase tuberculosis case detection. Burkina Faso has embarked on such an attempt. This study is a preliminary assessment of that model. METHODS: Qualitative data were collected using unstructured key informant interviews with policy makers, group interviews with THPs and health workers, and field visits to THPs. Quantitative data were collected from program reports and the national tuberculosis (TB) control database. RESULTS AND ANALYSIS: The distribution of tasks among THPs, intermediary organizations and clinicians is appealing, especially the focus on active referral. THPs are offered incentives based on numbers of suspected cases confirmed by health workers at the clinic, based on microscopy results or clinical assessment. The positivity rate was 23% and 9% for 2006 and 2007, respectively. The contribution of the program to national case detection was estimated at 2% for 2006. Because it relied totally on donor funding, the program suffered from irregular disbursements, resulting in periodic decreases in activities and outcomes. CONCLUSIONS: The study shows that single interventions require a broader positive policy environment to be sustainable. Even if the active referral approach seems effective in enhancing TB case detection, more complex policy work and direction, domestic financial contribution and additional evidence for cost-effectiveness are needed before the approach can be established as a national policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".