Herd health and production management visits on Canadian dairy cattle farms: Structure, goals, and topics discussed
Bibliographic record
Abstract
Regular veterinary visits to improve herd health and production management (HHPM) are important management components on many dairy cattle farms. These visits provide opportunities for constructive conversations between veterinarians and farmers and for shifting management from a reactionary approach to proactively optimizing health and welfare. However, little is known about the structure of HHPM farm visits and to what extent veterinarians provide assistance beyond purely technical services. Therefore, our aims in this cross-sectional study were to describe HHPM farm visit structure, determine which dairy-specific topics were discussed, and assess whether the focus of the visits aligned with farmers' priorities. Veterinary practitioners (n = 14) were recruited to record audio and video of regularly scheduled HHPM farm visits (n = 70) using an action camera attached to their chest or head. A questionnaire was distributed to farmers containing closed- and open-ended questions to assess their goals and perceptions related to farm management and HHPM farm visits. Descriptive statistics and negative binomial and Poisson regression models were used to study dairy-specific topics initiated by the farmer or veterinarian during various activities. A mean of 51% of the visit duration was dedicated to transrectal pregnancy and fertility diagnostics, and a considerable amount of time (30%) was spent on visit preparation, transitions between tasks, and leaving. A total of 488 discussions were initiated by either the veterinarian (55%) or the farmer (45%). Mean length of discussions was 2 min, and only 17% of the HHPM visit duration was spent discussing dairy-specific topics. Veterinarians initiated 62% of their discussions about herd issues, whereas farmer-initiated discussions revolved around herd health in 39% of the discussions. Discussion topics most frequently raised by participants included fertility, udder health, calf health and management, and transition diseases. Consistently, farmers' answers to a rank question regarding their main HHPM farm visit goals indicated that their priorities were to have transrectal pregnancy and fertility diagnostics performed and to improve herd fertility and general herd health. Answers to an open-ended question revealed that additional aims of many farmers were to receive information, have questions answered, and identify and discuss problems. A farmer's belief that HHPM farm visits were "absolutely" tailored toward his or her goals was positively associated with number of discussions during the visit and their conviction that they "always" voiced their wishes and needs to the veterinarian. Opportunities to broaden the focus of HHPM farm visits and improve communication between farmers and veterinarians should be identified and veterinarians should be trained accordingly, which would increase veterinarians' ability to add value during HHPM farm visits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".