Are informal healthcare providers knowledgeable in tuberculosis care? A cross-sectional survey using vignettes in West Bengal, India
Bibliographic record
Abstract
BACKGROUND: India accounts for one-quarter of the world's TB cases. Despite efforts to engage the private sector in India's National TB Elimination Program, informal healthcare providers (IPs), who serve as the first contact for a significant TB patients, remain grossly underutilised. However, considering the substantial evidence establishing IPs' role in patients' care pathway, it is essential to expand the evidence base regarding their knowledge in TB care. METHODS: We conducted a cross-sectional study in the Birbhum district of West Bengal, India. The data were collected using the TB vignette among 331 IPs (165 trained and 166 untrained). The correct case management was defined following India's Technical and Operational Guidelines for TB Control. RESULTS: Overall, IPs demonstrated a suboptimal level of knowledge in TB care. IPs exhibited the lowest knowledge in asking essential history questions (all four: 5.4% and at least two: 21.7%) compared with ordering sputum test (76.1%), making a correct diagnosis (83.3%) and appropriate referrals (100%). Nonetheless, a statistically significant difference in knowledge (in most domains of TB care) was observed between trained and untrained IPs. CONCLUSIONS: This study identifies gaps in IPs' knowledge in TB care. However, the observed significant difference between the trained and untrained groups indicates a positive impact of training in improving IPs' knowledge in TB care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".