Informal allopathic provider knowledge and practice regarding control and prevention of TB in rural Bangladesh
Bibliographic record
Abstract
BACKGROUND: BRAC (formerly Bangladesh Rural Advancement Committee), in collaboration with the National Tuberculosis Control Programme, provides one full-day training on TB to make informal allopathic providers knowledgeable for managing TB in rural Bangladesh. This study explored the knowledge and practices of the providers receiving the above training in the control and prevention of TB. METHODS: The study was conducted in 30 subdistricts, with 30 trained and 30 untrained providers randomly selected from each subdistrict. Approximately 3% (49/1800) did not provide complete information. Pre-tested structured and semi-structured questionnaires were used. RESULTS: TB was commonly perceived as a disease of only males (66.1%, 1157/1751). Only one-quarter knew about the bacterial cause of TB. Very few providers (2.1%, 36) had adequate knowledge regarding prevention of TB. They also lacked knowledge about TB treatment duration (71.6%, 1253), the meaning of DOTS (directly observed treatment, short course) (26.0%, 455) and multidrug resistance (20.6%, 360). Antibiotics (79.7%, 1396) and cough syrup (75.0%, 1313) were commonly prescribed by providers despite symptoms suggestive of TB. However, 70.2% (613) and 74.5% (650) of trained providers' knowledge and practice scores were equal to or more than the mean scores (≥6.97 and ≥6.6, respectively), whereas they were only 49.5% (435) and 64.2% (563), respectively, among untrained providers (p<0.0001). CONCLUSIONS: Misperception, lack of knowledge and irrational use of antibiotics are challenges that need to be addressed for controlling and preventing TB efficiently.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".