Addressing Individual Values to Impact Prudent Antimicrobial Prescribing in Animal Agriculture
Bibliographic record
Abstract
Antimicrobial resistance is a growing public health threat driven by antimicrobial use-both judicious and injudicious-in people and animals. In animal agriculture, antimicrobials are used to treat, control, and prevent disease in herds of animals. While such use generally occurs under the broad supervision of a veterinarian, individual animals are often treated by farm owners or managers. The decision to administer antimicrobials is therefore influenced not only by the clinical situation but also by the motivations and priorities of different individual actors. Many studies have examined the drivers of external forces such as costs, workload and time constraints, or social pressures on antimicrobial use by veterinarians and producers, but none have explored the role of individually held values in influencing decision-making related to antimicrobial use. Values are deeply held normative orientations that guide the formation of attitudes and behaviors across multiple contexts. Values have been shown to be strongly tied to perceptions of and attitudes toward polarizing topics such as climate change, and preliminary evidence suggests that values are also associated with attitudes to antimicrobial resistance and stewardship. In this article, we draw on lessons learned in other fields (human health care, climate change science) to explore how values could be tied to the extrinsic and intrinsic factors that drive antimicrobial use and prescribing in animal agriculture. We also provide suggestions for ways to build a bridge between the veterinary and social sciences and incorporate values into future research aimed at promoting antimicrobial stewardship in animal agriculture.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".