Why Veterinarians (Do Not) Adhere to the Clinical Practice Streptococcus suis in Weaned Pigs Guideline: A Qualitative Study
Bibliographic record
Abstract
The Netherlands has been very successful in the last decade in reducing antimicrobial use in animals. On about a quarter of farms, antimicrobial use in weaned pigs remains relatively high. As Streptococcus suis ( S. suis) infections are responsible for a high consumption of antimicrobials, a specific veterinary guideline to control S. suis was developed, but seemed to be poorly adopted by veterinarians. Guided by the theoretical domains framework, the aim of this study was to identify determinants influencing veterinarians' adherence to this guideline. We interviewed 13 pig veterinarians. Interviewees described multiple approaches to managing S. suis problems and adherence to the guideline. Mentioned determinants could be categorized into 12 theoretical domains. The following six domains were mentioned in all interviews: knowledge, skills, beliefs about capabilities, beliefs about consequences, social influences, and environmental context and resources. The insights derived from this study are relevant for understanding factors influencing veterinarians' adoption of scientific evidence and guidelines and can be used to develop evidence-based implementation strategies for veterinary guidelines.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".