Dairy producers' awareness, perceptions, and barriers to early detection and treatment of lameness on dairy farms: A qualitative focus group study
Bibliographic record
Abstract
Lameness is a common and painful condition, making it an important issue in the dairy industry. Whereas moderate and severe cases of lameness are likely to be noticed and dealt with by most dairy producers, mild cases are often overlooked. The barriers to implementing best management practices (BMP) to detect lameness are unknown. The objectives of this study were to understand awareness, perceptions, and barriers to implementation of established BMP for early detection and treatment of lameness of participant dairy farmers. In total, 35 dairy farmers from 2 regions of Ontario (southwestern [n = 3] and eastern [n = 3]) participated in 6 focus groups. Four themes were identified from the transcribed data: (1) perception and rationalization of lameness, (2) reconciling perceived effects and the ability to effect improvement, (3) assessment strategies, and (4) mild lameness detection challenges. Participants viewed the detection of lameness to be the responsibility of producers (i.e., themselves) and often disagreed with external assessors regarding the prevalence of lameness in their herds. They were unsure what the appropriate treatment was for mild lameness and questioned whether it had significant economic effects on their farms. Lameness assessments by producers occurred informally as participants performed other routine tasks. Some participants also reported using the interval between milkings in automatic milking systems as the primary lameness assessment method. Lack of training for employees and themselves, busy daily schedules, and continuously seeing the same cows were raised as important challenges to the detection of mild lameness. Our results suggest that participants viewed mild lameness detection and treatment a low priority with uncertain benefit. Greater recognition by dairy producers of the importance of early identification of lameness and improved access to effective treatment protocols will be needed to advance implementation of BMP for detection and treatment of nonsevere lameness.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".