A qualitative study of Ontario dairy farmer attitudes and perceptions toward implementing recommended milking practices
Bibliographic record
Abstract
Recommended milking practices (RMP) are protective against mastitis. However, many producers do not adopt, or only partially adopt, these measures. This study aimed to explore the attitudes and perceptions of Ontario dairy farmers toward barriers to implementation of RMP and to investigate what motivates behavior change in relation to milking hygiene. Four focus groups with Ontario dairy producers were conducted, and verbatim transcripts were analyzed thematically. The main barriers to adoption of RMP were identified and categorized into 2 groups: intrinsic barriers and physical barriers. Intrinsic barriers included personal habits and convenience, not perceiving udder health as a priority on their farm, and lack of information. Physical barriers included employee training and compliance, convenience of implementing RMP, and time, money, and labor barriers. Producers used their bulk tank somatic cell count (SCC) as a measure of perceived severity of udder health problems on farm. Those with lower SCC were less likely to prioritize udder health compared with peers experiencing elevations in SCC. Lack of udder health problems translated for some producers into non-adoption of certain RMP, as they felt these practices were not needed unless a problem arose. Others felt motivated to implement more practices and work toward better udder health if such efforts translated into rewards for better-quality milk. Some producers perceived RMP as not meaningful or useful, seemingly due to a lack of education about the reasons behind RMP implementation. Understanding the importance of these practices is one key to implementing them. To overcome some of the intrinsic barriers, increased efforts in knowledge translation are needed, including efforts in retraining current practices, as well as in establishing best 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".