DETERMINANTS OF PESTE DES PETITS RUMINANTS VACCINE ADOPTION AMONG FEMALE GOAT KEEPERS IN DHADING DISTRICT, NEPAL
Bibliographic record
Abstract
A study titled “determinants of Peste des Petits (PPR) vaccine adoption among female goat keepers in Dhading district, Nepal” was undertaken. We selected a sample of 120 household using random sampling technique. Altogether, 120 households were selected using the random sampling technique. Primary data were collected using a semi-structured and pre-tested household questionnaire, focus group discussion (FGD) and key informant interviews (KII) while secondary information was collected from different published records. Descriptive and inferential statistical tools were used to assess the collected information. We used the probit model, t-test, and chi square test were used to establish statistical relationships between variables. The probit model revealed that the heard size (27%) and income from goat rearing (51.15%) were significant at the 1% level of significance, while the education level of the respondents and their membership in a cooperative organization were significant at the 5% level of significance. However, ethnicity did not significantly influence the result. Among the ethnic groups; Brahmin and Chhetri women possess a greater understanding of the PPR vaccine, while Kumal and Dalit women are the least knowledgeable. A pairwise comparison revealed that among the 10 determinants, farmers with higher communication and education levels are more likely to adopt the PPR vaccine. Empowering women from marginalized tribes like Kumal and Dalit, involving them in cooperatives, and promoting the activities of animal health service providers and education are crucial factors in the adoption of PPR vaccines in goat farming
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".