Ontario dairy veterinarians' perspectives on down cow care, producer decision making, and implementation of recommended practices: A qualitative interview study
Bibliographic record
Abstract
The objective of this study was to examine Ontario dairy veterinarians' perspectives when working with producers on managing and caring for down dairy cows, focusing on protocols, current practices, implementation of best practices, and barriers. A total of 20 one-on-one online interviews with dairy veterinarians were conducted between July to December 2024, lasting a median of 30 min (range: 20-45 min). Veterinarians were asked to share their perspectives on a range of topics related to down cow management, including their experiences advising producers, observations of producer decision making, common care practices, and contextual challenges such as facility design and equipment use. The interviews were recorded, transcribed, and evaluated using applied thematic analysis in Quirkos (version 2.5.3, Quirkos Ltd., 2023). Four themes emerged from the data: producer traits and decision making; veterinarian-client relationships; care and management practices; and limits and logistics of care. Veterinarians described the influence of different producer attitudes on decision making, with proactive producers implementing preventive strategies, whereas others exhibited more resistance to change, often shaped by multigenerational traditions and economic constraints. Trust and communication emerged as critical components of veterinarian-client relationships, and it was acknowledged that these relationships take time to build. In terms of care and management, veterinarians emphasized timely intervention and welfare-focused approaches (e.g., providing pain medications and management practices that ensured cow comfort). Facilities and equipment were identified as major constraints to provide optimal down cow care, particularly in older barns with limited space and accessibility. Veterinarians also discussed euthanasia, addressing both the emotional challenges for producers and the practical criteria used to decide when to euthanize a down cow. By highlighting the complexities of producer decision making, veterinarian-client relationships, and care practices, this study provides insights into challenges and opportunities in managing down cows. These findings emphasize the veterinarian's role in advising producers through evidence-based management practices, fostering trust, and addressing practical barriers to implementing optimal care strategies.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".