Efficacy of continuous education and determinants of antimicrobials prescription behavior in companion animal veterinarians from Quebec, Canada
Bibliographic record
Abstract
Highly important antimicrobial use in veterinary companion animal medicine is frequent and in some cases unjustified. Establishing cautious and evidence-based prescription practices in veterinary companion animal medicine should be one of the key objectives of One Health antimicrobial stewardship initiatives. We aimed to (1) develop a training program on antimicrobial use in veterinary companion animal medicine in Quebec, Canada, (2) quantify the impact of this training on the use of highly important antimicrobials, and (3) identify the factors that could limit such an impact. A total of 54 veterinarians from 35 veterinary facilities participated in the study. Antimicrobials were identified from electronic medical records, prescription rates were calculated and a difference-in-differences estimation was used to compare prescription rates pre- and post-training in 2023, controlling for seasonal trends during the same period in 2022. Participating veterinarians prescribed on average 11.7 antimicrobial treatments per 100 consultations. Two thirds of the systemic antimicrobials prescribed by participants were antimicrobials of very high medical importance, according to Health Canada. Amoxicillin-clavulanic acid was the most often prescribed antimicrobial and accounted for approximately 22 % of all prescriptions. The training had a limited impact on overall prescription rates. However, a reduction of 55 % in prescription rate was observed for metronidazole, which is most often prescribed in canine patients with acute diarrhea. Interviews were conducted with 11 participating veterinarians to identify obstacles to the implementation of recommended prescription practices. The main obstacles identified were pet owner expectations, fear of therapeutic failure, lack of confidence in the recommendations and unavailability of therapeutic alternatives.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".