Impact of participatory training of smallholder pig farmers on knowledge, attitudes and practices regarding biosecurity for the control of African swine fever in Uganda
Bibliographic record
Abstract
We evaluated the impact of a participatory training of pig farmers on knowledge, attitude and practices (KAP) of biosecurity relating to ASF control in two districts of Uganda using a randomized control trial (RCT). A total of 830 pig farmers from 32 villages were included in the study, with 425 farmers receiving training, while 405 did not. An item response theory model was used to assess the impact of the training on farmer's KAP. Logistic regression models were used to assess the factors that affected knowledge gain and change in attitude and practices after training. Focus group discussions (FGD) were carried out with selected farmers from the treatment group at the end of the intervention, to share their experiences and discuss potential factors that could hinder adoption of biosecurity in their communities. Results of the regression analyses showed that there was a significant effect of biosecurity training (p = .038) on gain in knowledge after 12 months, but there were limited changes in farmer's attitude and practice at 12 and 28 months after training. Pig production domain (peri-urban/urban production), group membership, gender (male) and education of the farmer positively influenced knowledge gain and attitude of farmers towards biosecurity. This paper provides empirical evidence on the impact of training intervention on biosecurity practices for disease prevention or control. In addition, it breaks down the components of the biosecurity practices and documents the specific challenges to its uptake by the farmers. It therefore relaxes the assumption of knowledge constraint as a barrier to uptake. The results clearly show that knowledge is not the binding constraint to uptake of the biosecurity interventions.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".