Data_Sheet_1_Barriers and facilitators to implementing a new regulation restricting antimicrobial use in dairy production in Québec, Canada: A qualitative study.PDF
Bibliographic record
Abstract
<p>With the emergence of antimicrobial resistance (AMR), many countries are implementing restrictive regulations to reduce antimicrobial use (AMU) in animal production. Although these measures are effective at the national level, their implementation may generate challenges for producers and veterinarians. The objective of this study was to explore the barriers and facilitators of implementing a new regulation restricting the use of antimicrobials of very high importance for human health in the dairy production sector in the province of Québec, Canada. Individual interviews were conducted with fifteen veterinarians and twenty-seven dairy producers. Thematic analysis was performed based on the COM-B model of behavior change (capability-opportunity-motivation-behavior). Our results indicated that the lack of availability of alternative treatments, the long delays related to diagnostic tests and the fear of economic consequences were major barriers to the implementation of the regulation. A small number of producers also perceived that the regulation negatively impacted the health and wellbeing of their animals. Additionally, participants acknowledged the importance of early education and training to better understand the purpose of the regulation and increase its acceptability. Lastly, most participants reported that they had not only reduced their use of antimicrobials of very high importance for human health following the regulation, but they had also increased preventive practices on their farm. This study reveals that the implementation of restrictive regulations to reduce AMU in animal production can lead to multiple challenges in practice. Our results highlight the need for better communication and training of producers and veterinarians before and during the implementation of similar regulations in the future and underline the importance of measuring the direct and indirect impacts of those regulations on productivity and on animal health and wellbeing.</p>
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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 | 0.004 |
| 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) | 0.005 | 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".