Perceived barriers to implementation of biosecurity best management practices for control of Salmonella Dublin on dairy farms: A focus group study
Bibliographic record
Abstract
Salmonella Dublin is an emerging pathogen on Canadian dairy farms, making biosecurity practices crucial. However, the barriers to implementing biosecurity best management practices (BBMP) to control Salmonella Dublin (e.g., track herd health status, proactive response to disease risks, limit animal purchases, routine facility disinfection, control visitor access) are unknown. The objectives of this study were to understand dairy farmers' awareness, perceptions, and barriers to implementation of established BBMP for control of Salmonella Dublin. In total, 28 dairy farmers participated in 5 focus groups from 2 regions of Ontario (southwestern [n = 2] and eastern [n = 3]). Four themes were developed from the data: (1) informational preparedness, (2) structural factors that influence disease mitigation, (3) motivational drivers of disease mitigation, and (4) shifts in biosecurity engagement. Participants' perceived awareness and preparedness for an outbreak of Salmonella Dublin on their farm was variable. Although most participating farmers were aware of the pathogen, most were not concerned about their farm becoming infected and stated that their farm was not prepared to handle an outbreak. All focus groups discussed how structural factors including producer organizations and national or provincial governments could improve disease surveillance or how academia could create new farm-level best practices to mitigate Salmonella Dublin transmission. All focus groups also discussed intrinsic motivational drivers (e.g., that biosecurity does not register as a daily priority) and the belief that the risk of Salmonella Dublin infecting animals on their farms is low. Participants also discussed extrinsic barriers (e.g., money, labor, practicality) as challenges to change their biosecurity practices. Finally, participants who had experienced a Salmonella Dublin outbreak expressed a shift in their perspective toward biosecurity (proactive rather than complacent) and risk management (e.g., wanting to limit spread to other farms). Our results suggest that participants expected producer organizations, government, and academia to provide more support and develop strategies to mitigate the spread of Salmonella Dublin. Participants who had not experienced a Salmonella Dublin outbreak did not view the pathogen as a substantial risk to their farm. Therefore, until the perceived risk increases, the motivation to overcome extrinsic and intrinsic barriers to adopt BBMP to control this pathogen will likely remain low.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".