Dog Owners’ Attitude toward Veterinary Antibiotic Use and Antibiotic Resistance with a Focus on Canine Diarrhea Management
Bibliographic record
Abstract
An ad hoc questionnaire was designed in order to investigate AMR knowledge amongst Italian dog owners, owner expectations concerning pharmacological treatment of canine AD, and client attitudes towards and compliance with alternative strategies to antimicrobial administration. A total of 250 questionnaires were returned. Most of respondents were female, aged 36–70 and workers. More than a half of participants owned one dog with mixed breed, with Labrador retriever, golden retriever, dachshund, and border collie being the most represented breeds. On average, each dog was treated with an oral antibiotic 1.044 times per year. Intestinal diseases were among the main reasons (19%) for antibiotic prescription. Oral antibiotic courses without veterinary consultation (21%) and anticipated termination of the therapy (17.1%) were less common than reported elsewhere. The majority of respondents knew the meaning of AMR with a significant inverse association between the level of education and the tendency to administer antimicrobials without consulting a clinician (p = 0.004). Most of the owners expected a rapid recovery of clinical signs after a first episode of AD and accepted natural dietary supplementation for treating the condition. Ninety-five percent of the respondents believed that public funding should be spent to study AMR. Even though an acceptable degree of AMR awareness emerged, we feel that further efforts should be made to increase public AMR knowledge and to stimulate proactive measures to fight the phenomenon. On the other hand, the development of guidelines for the treatment of uncomplicated canine AD would help clinicians to rationalize antimicrobial use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".