Investigating Ontario dairy farmers motivations and barriers to the adoption of biosecurity and Johne's control practices
Bibliographic record
Abstract
For the control of Johne's disease (JD), management practices to minimize disease transmission must be implemented and maintained. Once infected, animals will enter a latent phase and will typically only manifest clinical symptoms years later. As young calves are the main susceptible group on farm, the observed effects of management practices geared toward minimizing their exposure to infective material may not be realized until years later. This delayed feedback limits the sustained implementation of JD control practices. Although quantitative research methods have demonstrated changes to management practices as well as their association with changes to JD prevalence, dairy farmers can offer insights into the current challenges relating to JD implementation and control. Thus, this study aims to use qualitative methods and in-depth interviews (n = 20) with Ontario dairy farmers who had previously been engaged in a Johne's control program to explore their motivations and barriers to the implementation of JD control practices and general herd biosecurity. A thematic analysis using inductive coding was completed generated the following 4 overarching themes: (1) the hows and whys of Johne's control, (2) barriers to general herd biosecurity, (3) barriers to Johne's control, and (4) overcoming barriers. Farmers no longer believed JD was an issue on their farm. Johne's was low on their list of concerns due to little public discourse, absence of animals displaying clinical signs, and no financial support for diagnostic testing. Producers who were still actively engaged in JD control cited animal and human health as their primary motivations. Financial support, targeted education, and promoting engagement through discourse may help encourage producers to reconsider their participation in JD control. Government and industry collaboration with producers may help to develop more effective biosecurity and disease control programs.
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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 | 0.005 | 0.010 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".