Contextualized understandings of dairy farmers' perspectives on antimicrobial use and regulation in Alberta, Canada
Bibliographic record
Abstract
Antimicrobial resistance (AMR) has been largely attributed to antimicrobial use (AMU). To achieve judicious AMU, much research and many policies focus on knowledge translation and behavioral change mechanisms. To address knowledge gaps in contextual drivers of decisions made by dairy farmers concerning AMU, we conducted ethnographic fieldwork to investigate one community's understanding of AMU, AMR, and associated regulations in the dairy industry in Alberta, Canada. This included participation in on-farm activities and observations of relevant interactions on dairy farms in central Alberta for 4 mo. Interviews were conducted with 25 dairy farmers. The interviews were analyzed using thematic analysis and yielded several key findings. Many dairy farmers in this sample: (1) value their autonomy and hope to maintain agency regarding AMU; (2) have shared cultural and immigrant identities which may inform their perspectives of future AMU regulation as it relates to their farming autonomy; (3) feel that certain AMU policies implemented in other contexts would be impractical in Alberta and would constrain their freedom to make what they perceive to be the best animal welfare decisions; (4) believe that their knowledge and experience are undervalued by consumers and policy makers; (5) are concerned that the public does not have a complex understanding of dairy farming and, consequently, worry that AMU policy will be based on misguided consumer concerns; and (6) are variably skeptical of a link between AMU in dairy cattle and AMR in humans due to their strict adherence to milk safety protocols that is driven by their genuine care for the integrity of the product. A better understanding of the sociocultural and political-economic infrastructure that supports such perceptions is warranted and should inform efforts to improve AMU stewardship and future policies regarding AMU.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".