Barriers to the hospital treatment among Bede snake charmers in Bangladesh with special reference to venomous snakebite
Bibliographic record
Abstract
Snakebite envenoming is a potentially life-threatening global public health issue with Bangladesh having one of the highest rates of snakebite cases. The Bede, a nomadic ethnic group in Bangladesh, traditionally engages in snake-related business such as snake charming. The Bede relies on their own ethnomedicinal practitioners for snakebite treatment while there is a lack of concrete evidence on the effectiveness of such ethnomedicinal treatment. To identify the barriers to the utilization of biomedical treatment for snakebite we conducted interviews with 38 Bede snake charmers, who have experienced snakebite, and six family members of those who died of snakebite. Our results show that four critical barriers, Accessibility, Affordability, Availability, and Acceptability (4As), prevented some of the Bede from seeking biomedical treatment. Moreover, we found that a few Bede died of a snakebite every year. There are survivors of snakebite who were able to receive biomedical treatment by overcoming all of the 4As. Our results provide insights into the current state of snakebite treatment in Bangladesh and can inform the development of more effective and accessible treatment options for those affected. Partnership between the public sector and the Bede community has the potential to make a significant impact in reducing snakebite morbidity and mortality in Bangladesh.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".