Evaluating the Efficacy of Teaching Methods Regarding Prevention of Human Epilepsy Caused by Taenia solium Neurocysticercosis in Western Kenya
Bibliographic record
Abstract
Taenia solium neurocysticercosis is a major cause of adult-onset epilepsy in developing countries. A questionnaire was administered to 282 Kenyan farmers, followed by a workshop, a second questionnaire, one-on-one training, and a third questionnaire. People who attended workshops were more likely to know how T. solium causes epilepsy in humans in the third visit than the second (P = 0.001). The likelihood that farmers would tether their pigs 100% of the time, limiting exposure to tapeworm eggs, increased after the first (P < 0.001) and second visits (P < 0.001). Farmers were more likely to have heard of Cysticercus cellulosae in the second (P = 0.001) and third visits (P = 0.007), and to know how pigs acquire infection in the second (P = 0.03) and third visits (P = 0.003). Farmers with at least a grade 8 education were more likely to know how T. solium is transmitted to humans in the second (P = 0.001) and third visits (P = 0.009), and were more likely to understand the relationship between epilepsy and T. solium in the second (P = 0.03) and third visits (P = 0.03). Grade 8 education may enhance learning from written material. Workshops followed by individual on-farm training enhanced knowledge acquisition and behavior changes. Training local government extension workers contributed to the sustainability of this project.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".