Factors affecting cattle producers’ willingness to adopt an Escherichia coli O157:H7 vaccine: a probit analysis
Bibliographic record
Abstract
E. coli O157:H7 bacteria – a major cause of foodborne illness – occur naturally in the intestine of cattle but do not affect the health or productivity of the animal. A cattle vaccine that significantly reduces the risk of E. coli contamination was developed and commercialized in Canada and internationally, however, adoption by cattle producers remained extremely low. Utilizing data from a survey of cow-calf producers in western Canada, this paper examines the factors affecting cattle producers’ willingness to adopt the E. coli vaccine. Education, prior awareness of the vaccine, perception of who holds primary responsibility for E. coli risk reduction, and a producer’s external (versus internal) locus of control with respect to their ability to mitigate E. coli risks within the production environment are significant determinants of willingness to adopt. Adoption incentives are also evaluated, including policy interventions, market/supply chain incentives, production protocol, and producer reputation incentives. The analysis provides lessons for the development and commercialization of vaccines and other food safety intervention strategies that yield societal and supply chain benefits beyond the individual adopter.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".