Producer attitudes regarding antimicrobial use and resistance in Canadian cow-calf herds.
Bibliographic record
Abstract
Objective: To describe producer attitudes toward antimicrobial use (AMU) and antimicrobial resistance (AMR), identify factors associated with attitudes, and inform stewardship initiatives. Animal: Beef cattle, cow-calf. Procedure: = 146) on producers' attitudes toward AMU, AMR, and impacts of recent regulatory changes requiring a prescription for the purchase of medically important antimicrobials (MIA). Results: Most producers (78%, 114/146) reported being aware of initiatives to improve antimicrobial stewardship within the beef industry and 67% (97/146) indicated that AMR was a highly important issue to the industry and producers personally. Almost half of producers reported concerns that AMR development has impacted AMU decisions on their operations. Overall, veterinarians were producers' primary source of information regarding AMU, including treatment protocols, stewardship programs, and regulatory changes. Following introduction of the 2018 prescription-only regulations, 95% (138/146) of producers reported no change in AMU on their operations. Similarly, 77% (112/146) of producers reported no change in antimicrobial product access, whereas 63% (91/146) reported no change in cost. Conclusion: Most producers reported little change in access to antimicrobials and in AMU following the introduction of regulations requiring a prescription for MIA. Clinical relevance: Producers rely on veterinarians as their primary source of information regarding antimicrobial regulations and AMU. It is therefore important for veterinarians to understand their role as educators for beef cow-calf producers. Ultimately, veterinarians and producers need to work together to ensure that the health and welfare of animals are protected while using antimicrobials in a responsible manner.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".