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Record W4388822346 · doi:10.1371/journal.pntd.0011768

“I sold my towel and shoes to pay the traditional healer”: Care-seeking costs and productivity losses among snakebite victims in Eastern Province, Rwanda

2023· article· en· W4388822346 on OpenAlexaff
Janna M. Schurer, Mahlet Tadesse Admasu, Mihigo Bonaventure, Dieudonne Hakizimana, Elijah Murara, Lauren E. MacDonald, Ellen Rafferty

Bibliographic record

VenuePLoS neglected tropical diseases · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVenomous Animal Envenomation and Studies
Canadian institutionsUniversity of Alberta
FundersRoyal Society of Tropical Medicine and HygieneWorld Health Organization
KeywordsPovertyProductivityMedicineEnvironmental healthSocioeconomicsBusinessEconomic growthEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.230
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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