Ontario dairy producers' and veterinarians' perspectives: Barriers to biosecurity implementation
Bibliographic record
Abstract
Implementing biosecurity protocols is necessary to reduce the spread of disease on dairy farms. In Ontario biosecurity implementation is variable among farms, and the barriers to implementing biosecurity are unknown. Thirty-five semistructured interviews were conducted between July 2022 and January 2023 with dairy producers (n = 17) and veterinarians (n = 18). Participants also completed a demographic survey. Thematic analysis was performed with constructivist and grounded theory paradigms. Thematic coding was done inductively using NVivo software. Dairy producers' understanding of the definition of biosecurity varied, with all understanding that it was to prevent the spread of disease. Furthermore, the most common perception was that biosecurity prevented the spread of disease onto the farm. Both veterinarians and producers stated that closed herds were one of the most important biosecurity protocols. Barriers to biosecurity implementation included a lack of resources, internal and external business influencers, individual perceptions of biosecurity, and a lack of industry initiative. Understanding the barriers producers face provides veterinarians with the chance to tailor their communication to ensure barriers are reduced or for other industry members to reduce the barriers.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".