Survey of antimicrobial prescribing practices across western Canadian beef cow-calf veterinarians.
Bibliographic record
Abstract
Objective: This study was to determine if the prescribing practices of western Canadian beef cow-calf veterinarians changed after Canada revised federal regulations in 2018 to require veterinary prescriptions for all medically important veterinary antimicrobials. Animals: Beef cattle, cow-calf. Procedure: An electronic survey was used to capture onboarding of new clients and to record herd health information, dispensing of antimicrobials after hours, reported client concerns with the regulation changes, and basic veterinary practitioner demographics such as province of licensure and number of years in practice. Seventy-two western Canadian veterinarians completed the survey in the winter of 2024. Results: After 2018, the frequency of onboarding of new clients increased, as did herd health data capture. Most participants (80%) reported spending more time supporting client needs for antimicrobial prescriptions after 2018, with 63% reporting more beef cow-calf clients needing this service and 39% reporting the acquisition of new beef cow-calf clients. Billing for onboarding as professional time increased after 2018 but the relative frequencies of methods for dispensing antimicrobials after hours did not change. Changes in prescribing veterinary antimicrobials after 2018 included a decrease in sulfonamides and increases in tetracyclines and phenicols. Reported changes in client antimicrobial use included decreases in penicillin and sulfonamides and increases in phenicols and macrolides. Conclusion and clinical relevance: Although veterinarians reported challenges in meeting client needs and complying with the regulatory change, their comments were largely neutral to positive regarding the effect of the changes. Suggestions from veterinarians included the development of tools to support prescribing and to track client antimicrobial inventory, client antimicrobial use, and disease incidence.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".