Characterizing the attitudes and motivations of Ontario dairy producers toward udder health
Bibliographic record
Abstract
Considerable research has focused on identifying risk factors for intramammary infections, yet mastitis remains a pervasive disease on dairy farms. Increasingly, researchers are appreciating the role of dairy producer mindset in determining management style and thus udder health status of the herd. The objective of this study was to explore the attitudes and motivations of Ontario dairy farmers toward udder health in herds with varying bulk milk somatic cell count (BMSCC). In December 2011, 5 focus groups were conducted across Ontario, Canada, with independent groups of dairy producers representing low, medium, and high BMSCC herds. Groups were established based on producer's weighted BMSCC levels as recorded over the summer of 2011. A semi-structured interview guide was followed to discuss topics relating to udder health. Thematic analysis was performed on the interview transcripts. Generally, producers noted management techniques (specifically culling infected cows and monitoring BMSCC), a perceived wealth of information on mastitis control, and a proactive whole-herd management approach engender the perception of control over mastitis. Producers in the low BMSCC group were confident in their level of knowledge and control of mastitis in their herds, whereas high BMSCC producers generally felt lower levels of control. Several areas were identified by producers that counteract this perception, contributing to perceived low levels of control over mastitis. Participants identified that at certain times they do not understand the cause of BMSCC on their farm. This attitude was especially prominent in the high BMSCC group. Other times, producers cited improper sample handling, seasonal issues, perceived milk culture shortcomings, and low herd size as factors that limited their control over mastitis in their herds. Though producers generally have high levels of self-efficacy beliefs when it comes to udder health management, the perception still exists that, under certain situations, mastitis is uncontrollable. This highlights the fact that educational and extension efforts need to focus on ensuring that producers employ proven mastitis diagnostic, prevention, and treatment practices in a systematic manner, with realistic expectations.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".