Perceived barriers of dairy producers to the adoption of selective antimicrobial therapies for nonsevere clinical mastitis and at dry-off in dairy cattle: A focus group study in Ontario, Canada
Bibliographic record
Abstract
Prudent antimicrobial use (AMU) in the dairy industry is crucial as it affects animal health and welfare and could help to slow the development of antimicrobial resistance. There is a need to adopt selective AMU. However, the barriers to adoption of selective antimicrobial use for the management of mastitis and dry-off are not adequately described. The objective of this study was to understand the barriers that dairy farmers in Ontario faced in the adoption of selective antimicrobial therapy for nonsevere clinical mastitis and at dry-off in dairy cattle. Six focus groups were held in 2 regions of Ontario (southwestern [n = 3] and eastern [n = 3]) involving 35 dairy farmers. Three themes were identified from the transcribed discussions: (1) experiences with selective antimicrobial mastitis and dry-off therapies, (2) risk tolerance for selective antimicrobial mastitis and dry-off therapies, and (3) factors influencing the adoption of selective antimicrobial mastitis and dry-off therapies. Participants viewed the decision to adopt selective antimicrobial mastitis and dry-off therapies to be the responsibility of the dairy producer. They described the use of bacterial diagnostics for selective treatment of nonsevere clinical mastitis as frustrating because results were not delivered in time to inform treatment. Some participants were not receptive to selective dry-off therapy because they perceived it placed their cows at high risk for mastitis during the next lactation. Participants who used selective dry-off therapy often mitigated these initial concerns by beginning this strategy with a small group of low-production animals. Individual cow data from automatic milking systems and record keeping were viewed as instrumental to the success of selective AMU but human elements (e.g., visual assessment of animals) continued to be used in the decision-making process. Some participants described cognitive dissonance and a reluctance to change when selective AMU to manage mastitis appeared to be in conflict with previously recommended blanket treatment practices. Our results suggest that cognitive dissonance experienced by participants may be mitigated by information from trusted sources, such as veterinarians. Additionally, peer-to-peer learning opportunities (e.g., dairy producers learning from colleagues' experiences and reflecting on their own current practices) could be used to facilitate evaluation of whether adoption of selective AMU aligns with their management approach for clinical mastitis. Therefore, until rapidity of mastitis diagnostics and communication of results improves for selective lactation therapy, and the perceived mastitis risk related to selective dry-off therapy is addressed, challenges will continue for the adoption of best management practices for selective antimicrobial therapy for nonsevere clinical mastitis and at dry-off in dairy cattle.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".