Determinants of Voluntary Vaccination Against Endemic Infectious Pig Diseases in the Philippines
Bibliographic record
Abstract
Smallholder pig farmers’ decision to adopt a vaccination strategy can be effective in managing threats associated with endemic infectious diseases in pig production. However, there is a significant knowledge gap regarding farmers’ voluntary use of vaccines to reduce the burden of endemic pig diseases. This indicates that a better understanding of farmers’ decisions to adopt disease management strategies is necessary. To determine the prevalence of voluntary veterinary vaccine use among smallholder pig farmers, we administered a survey using a multiple price list approach paired with a structured questionnaire to 141 pig raisers in Pampanga, Philippines. Multinomial logistic regression analysis was used to determine the factors associated with level of voluntary vaccine use. Results show a moderate voluntary uptake of veterinary vaccines among smallholder pig raisers at 51.7%, with 41.8% using one type of veterinary vaccine and 9.9% using two types. This estimate is higher than figures reported in other countries, suggesting that smallholder pig farmers value the benefits derived from vaccination despite limited government support. The study also demonstrates that risk preference affects farmers’ decisions to use veterinary vaccines. Risk preference should be taken into consideration in developing strategies to deliver knowledge and support about animal vaccines. For instance, risk seeking farmers may require targeted support to adopt more than one veterinary vaccine strategy. It is evident that smallholder pig farmers need considerable guidance and support to choose effective strategies for managing production diseases, thereby enabling their continued participation in the swine sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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".