Biosocial Complexities of Antimicrobial Use in Dairy Farming in Alberta, Canada
Bibliographic record
Abstract
Antimicrobial resistance (AMR), or the ability of a microbe to withstand treatment with antibiotics, is an emerging health issue that has been largely attributed to the inappropriate use of antimicrobial treatments. Many of the current research and policy initiatives focus on knowledge translation and behavioral change mechanisms as ways to achieve absolute reductions in antimicrobial use across all health sectors. However, the current approach fails to address underlying drivers of practice and is narrowly focused on achieving a numeric goal. Given the failure to understand the underlying drivers of decisions made by dairy farmers concerning antimicrobial use (AMU), this study sought to understand one community’s perceptions surrounding AMU, AMR, and regulation in the dairy farming industry in Alberta via the use of ethnography. Specifically, this included participation in on-farm activities (i.e., milking) and observations of relevant interactions (i.e., herd health exams) on dairy farms in Central Alberta for a period of 3.5 months. Interviews were conducted with 25 dairy farmers. Nine of these interviews were analyzed using thematic analysis. Thematic analyses resulted in four key takeaways. Farmers: 1) feel that AMU policies implemented in other contexts are impractical and are concerned that such policies, if implemented in Alberta, would constrain their freedom to make what they perceive to be the best decisions about AMU for their animals; 2) believe that their first-hand knowledge is undervalued by both consumers and policy-makers; 3) do not believe that the public trusts them to make the correct AMU choices and, consequently, worry that AMU policy will be guided by what they believe are misguided consumer concerns; 4) farmers are skeptical of a link between AMU in livestock and AMR in humans. Based on these findings, a better understanding of the sociocultural and political-economic infrastructure that supports such perceptions is warranted and should inform future policy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".