Redefining on-farm practices: The perceived effect of a responsible antimicrobial use regulation on dairy farmers
Bibliographic record
Abstract
The global food animal industry faces a growing concern regarding antimicrobial resistance (AMR), primarily driven by the use of antimicrobials (AM) for the treatment, control, and prevention of diseases. Addressing this challenge requires promoting responsible antimicrobial use (AMU) practices. In 2019, the province of Québec, Canada, took a significant step by implementing a regulation that limits the use of AM of very high importance for human medicine (category I AM as defined by Health Canada) in the food animal industry. However, the implementation of such regulation can significantly influence behavioral shifts among producers, contributing to the wider effort against AMR. Therefore, the objective of this observational study was to describe the perceived changes in dairy producers' knowledge and on-farm practices following the implementation of this regulation, using a cohort design. Data collection involved administering questionnaires to 87 dairy producers from 3 regions of the province of Québec (Estrie, Montérégie, Centre-Du-Québec) before (2017-2018) and after (2020-2021) the implementation of the regulation. The questionnaires explored the descriptive characteristics of farms, the knowledge of producers about the categorization of AM, the on-farm treatment practices, and the perceived effects of the regulation. Statistical analysis included t-tests and McNemar tests to compare the paired data obtained using the 2 questionnaires. The results indicated an increase in the knowledge score (the number of AM correctly categorized by the producers by their importance for human medicine) after the implementation of the regulation, suggesting an improved understanding of the categorization of AM based on their importance for human medicine. Trends in AMU practices for treating clinical mastitis and reproductive diseases suggested that category I AM were less likely to be reported as the primary treatment after the regulation, whereas category II AM were more often reported as primary treatment. Adoption of the selective dry cow therapy method significantly increased, whereas the use of teat sealants remained unchanged. Moreover, producers had divergent perceptions regarding the effect of the regulation on the cure rates and disease frequencies. This disparity emphasizes the need for comprehensive data collection to discern the risks associated with such regulatory shifts. The study acknowledges several limitations, including the potential for recall bias, confirmation bias, and desirability bias. Despite these limitations, this study shows that implementing regulations to encourage responsible AMU drives positive transformations in producers' knowledge and on-farm practices. This underscores the pivotal effect of proactive interventions in combating the escalating threat of AMR within the global food animal industry.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".