“I sold my towel and shoes to pay the traditional healer”: Care-seeking costs and productivity losses among snakebite victims in Eastern Province, Rwanda
Bibliographic record
Abstract
Snakebite envenomation (SBE) is endemic to sub-Saharan Africa and generally over-represented in rural, remote, and impoverished agricultural communities. While poverty is an established risk factor, little research has been done to investigate the economic consequences of SBE. This cross-sectional, quantitative study aimed to measure out-of-pocket spending and lost income when a household member was bitten by a snake. In 2020, 732 snakebite survivors from Eastern Province (Rwanda) agreed to complete a survey administered by telephone. The survey focused on participant demographics, income, direct medical and non-medical costs, care-seeking decisions, and lost work during convalescence. Our results suggested that patients incurred the highest mean expenses when they sought care from hospitals (11 307 RWF or 12 USD) or traditional healers (5 836 RWF or 6 USD) but that the highest maximum cost was incurred from traditional healers (300 000 RWF or 313 USD). Across all victims, the total amount paid to traditional healers (3.4 million RWF or 3 537 USD) was 4.7 times higher than all other care providers combined. On average, families lost 111 814 RWF (117 USD) per snakebite in direct treatment costs and indirect productivity losses. Many victims sought care from traditional healers despite being eligible for free medical care. Altogether, this study serves as a reminder of the serious physical and financial consequences associated with SBE and provides justification for new investments into SBE prevention and 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".