Integration of Tsetse Fly and Trypanosomiasis Control Methods From Livestock Farmers’ Perspective: A Multivariate Probit Approach
Bibliographic record
Abstract
Integration of tsetse fly and trypanosomiasis control methods is identified as most feasible and effective approach to eradication of African Animal Trypanosomiasis (AAT) and Human African Trypanosomiasis (HAT). However, little focus is directed towards understanding the drivers of integration of the control methods by farmers. We used cross-sectional data collected from 536 livestock keeping households in Lamu County of Kenya to identify factors influencing multiple use of insecticide treated livestock (ITL), insecticide treated targets (ITT), and treatment with trypanocidal drugs (TTD). Multivariate probit model was applied in estimation of covariates of multiple use of the control methods. Descriptive results indicated that nearly 61% of the livestock keeping households used at least one of the tsetse fly and trypanosomiasis control methods, with about 9%, 7%, and 13% of the households using ITL, ITT, and TTD, respectively. The results also indicated that nearly 32% of the households integrated the control methods. Furthermore, multivariate probit results showed that sex of household head, age of farmer, positive perceptions of technology availability and effectiveness, and off-farm income increased the likelihood of integration. In contrast, household size, having agriculture as the main occupation, and cost of the technology significantly reduced the likelihood of multiple use of the control methods. The results suggest heterogeneity in farmers’ decisions to integrate tsetse fly and trypanosomiasis control methods. Therefore, farmer outreach programs should consider key household characteristics, as well as technological attributes which may stimulate adoption of appropriate tsetse fly and trypanosomiasis control technologies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".