Disease Control on Dairy Farms with a Focus on Johne's Disease and Veterinary Communication
Bibliographic record
Abstract
Motivating dairy farmers to implement disease prevention and control strategies can be challenging. The first objective of this thesis was to assess factors that influence farmers’ management decisions. A literature review was conducted focusing on socio-psychological influences and farmers’ preferred information sources. Additionally, surveys and qualitative interviews with Alberta dairy farmers contributed to the current knowledge by assessing farmers’ attitudes, beliefs, and perceptions in regard to the prevention and control of Johne’s disease, an infectious enteritis that is caused by Mycobacterium avium subsp. paratuberculosis (MAP) and is endemic in Canadian dairy cows. Environmental fecal samples were analyzed for MAP to assess whether farm infection status influenced farmers’ decision to enroll in the voluntary Alberta Johne’s Disease Initiative (AJDI). Observed herd prevalence of MAP (i.e., 51%) was similar between AJDI participants and nonparticipants. Results further indicated that farmers have to believe in the importance of the disease and in recommended prevention and control strategies to make changes. In Alberta, the reasons why farmers did not participate in the AJDI or implemented recommended measures included skepticism of the threat and negative effects of Johne’s disease, critique of test sensitivity, required time, and costs. Farmers used a variety of information channels, but herd veterinarians had a major influence on their management. Veterinarians are in an ideal position to communicate and motivate recommended strategies targeted to each farm, and it is well established that effective communication skills can improve adherence with advice and health outcomes. Therefore, the second objective was to assess veterinary communication patterns. First, the suitability of on-farm video recordings for comprehensive communication analysis using the Roter Interaction Analysis System was demonstrated. Then, veterinary communication during 70 dairy farm visits was assessed. Veterinarians spent most of their talk on farmer education and relationship building. Demographics such as gender and length of the professional veterinarian-farmer relationship affected the use of some communication variables, whereas the effect of previous communication training was minimal. Identification of influences on farmers’ management decisions and of veterinary communication patterns can reveal opportunities to enhance communication, thus improving the uptake of prevention and control measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".