Knowledge of Zoonoses Among Those Affiliated with the Ontario Swine Industry: A Questionnaire Administered to Selected Producers, Allied Personnel, and Veterinarians
Bibliographic record
Abstract
Zoonoses are diseases caused by infectious agents that are transmitted from animals to humans. Some zoonoses have been associated with the pig and pork industry. To ensure the safety of pigs and pork and to improve public health it is essential to understand the level of knowledge of those affiliated with the swine industry. The purpose of our study was to assess the knowledge of and attitude toward zoonotic and other microbial hazards among targeted groups of stakeholders associated with the Ontario swine industry. A postal questionnaire was sent to 409 individuals representing producers, veterinarians, and allied industry personnel. The questionnaire included seven dichotomous and Likert-scale type questions on microbial hazards, addressing topics on familiarity, concern, presence, antimicrobial resistance, and knowledge transfer. The overall response rate was 53% (218/409). More respondents were concerned about the zoonotic potential of Salmonella spp. (53-94%) and swine influenza virus (64-75%) than other hazards. The group of veterinarians were more familiar (>89%) with all microbial hazards than other occupation groups. Additionally, antimicrobial resistance was reported as a problem by more (60%) veterinarians than producers (20%). Educational efforts should focus on preferred methods of knowledge transfer (e.g., producer meetings, magazine) to update swine industry personnel about zoonoses in an attempt to improve food safety and public health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".