Understanding barriers to veterinary involvement in dairy calf health management
Bibliographic record
Abstract
The objectives of this cross-sectional study were to identify barriers to veterinary involvement in calf health and assess knowledge gaps in calf care among American and Canadian bovine veterinarians. A questionnaire was administered to veterinarians, collecting data on demographics, satisfaction with calf health management knowledge, involvement in decision-making, satisfaction with calf health involvement, frequency of calf health record analysis and feedback, topics of interest for further learning, and preferred learning formats. Multivariable logistic regression models were used to assess associations between variables and outcomes. Only 28% of veterinarians frequently reviewed calf health records, and 44% made actionable recommendations after reviewing them. Female veterinarians were more likely than male veterinarians to frequently review calf health records (Odds ratio - OR: 2.9, 95% CI: 1.2-7.3). Additionally, the odds of frequently reviewing records increased with the amount of time spent working with calves (OR: 10.2 per 10% increment, 95% CI: 10.0-10.5). Veterinarians highly satisfied with their knowledge of neonatal calf diarrhea (NCD) prevention were more likely to make recommendations based on records (OR: 11.6, 95% CI: 1.9-72.4). Additionally, those frequently reviewing records were more likely to provide feedback (OR: 15.5, 95% CI: 4.0-60.3). Incomplete records was the most common reason for not reviewing records (60% of respondents) and why actionable recommendations were made less frequently than "most of the time" (67% of respondents). Veterinarians were least confident in their knowledge regarding milk feeding and weaning recommendations but they were interested in learning more about post-weaning nutrition and automated calf feeders. Further, they preferred conference presentations for continuing education. These findings suggest that veterinary involvement in calf health could be improved by facilitating better data capture and enhancing veterinarian knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".