“We do not bury dead livestock like human beings”: Community behaviors and risk of Rift Valley Fever virus infection in Baringo County, Kenya
Bibliographic record
Abstract
BACKGROUND: Rift Valley Fever (RVF), is a viral zoonotic disease transmitted by Aedes and Culex mosquitoes. In Kenya, its occurrence is associated with increased rains. In Baringo County, RVF was first reported in 2006-2007 resulting in 85 human cases and 5 human deaths, besides livestock losses and livelihood disruptions. This study sought to investigate the county's current RVF risk status. METHODOLOGY AND PRINCIPAL FINDINGS: A cross-sectional study on the knowledge, attitudes and practices of RVF was conducted through a mixed methods approach utilizing a questionnaire survey (n = 560) and 26 focus group discussions (n = 231). Results indicate that study participants had little knowledge of RVF causes, its signs and symptoms and transmission mechanisms to humans and livestock. However, most of them indicated that a person could be infected with zoonotic diseases through consumption of meat (79.2%) and milk (73.7%) or contact with blood (40%) from sick animals. There was a statistically significant relationship between being male and milking sick animals, consumption of milk from sick animals, consuming raw or cooked blood, slaughtering sick livestock or dead animals for consumption (all at p≤0.001), and handling sick livestock with bare hands (p = 0.025) with more men than women engaging in the risky practices. Only a few respondents relied on trained personnel or local experts to inspect meat for safety of consumption every time they slaughtered an animal at home. Sick livestock were treated using conventional and herbal medicines often without consulting veterinary officers. CONCLUSIONS: Communities in Baringo County engage in behaviour that may increase their risk to RVF infections during an outbreak. The authors recommend community education to improve their response during outbreaks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".