Attitudes of Canadian dairy farmers toward a voluntary Johne's disease control program
Bibliographic record
Abstract
The success of Johne's disease (JD) control programs based on risk assessment (RA) depends on producers' compliance with suggested management practices. One objective of this study was to describe the perception of participating Canadian dairy farmers of the impact of JD, the RA process, and suggested management strategies. The second objective was to describe the cost of changes in management practices following the RA. A telephone survey was conducted with 238 dairy farmers in Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia. The producers agreed to participate in this follow-up study after they had been enrolled in an RA-based voluntary JD control program and had tested their herd with the JD milk ELISA test in 2005 to 2007. The majority of farms had no JD test-positive cows and, although some producers thought they had experienced the economic impact of JD, many did not see JD as a current problem for their herd. The majority of producers enrolled in this program because they were concerned that Mycobacterium avium ssp. paratuberculosis could be perceived by consumers as a cause for Crohn's disease in humans, which could lead to altered purchasing behavior of milk and milk products. Fifty-two farm-specific recommendations had been made after the initial RA. Although the producers generally liked the program and found the recommendations reasonable and feasible, on average only 2 of 6 suggestions made specifically to them were implemented. The recommendation with the highest compliance was culling of JD test-positive cows. The main reasons for noncompliance were that the dairy producer did not believe a change of management practices was necessary or the available barn setting or space did not allow the change. Producers were generally uncomfortable estimating time and monetary expenses for management changes, but found that several suggested management practices actually saved time and money. In addition, 39% of the producers that implemented at least 1 recommendation thought their calf and herd health had improved subsequently. This indicates that the communication of associated benefits needs to be improved to increase the compliance of producers with recommended management practices.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".