A cross-sectional survey examining Canadian veterinarians' experiences with and perceptions of canine importation.
Bibliographic record
Abstract
Background: As numbers of dogs entering Canada from abroad are increasing, it is essential to understand the scope, challenges, and risks associated with canine importation. Canadian veterinarians' collective experience with and clinical knowledge of imported dogs can provide valuable insights into this practice. Objective: To describe Canadian veterinarians' experiences with, and perspectives of, imported dogs in clinical practice. Procedure: national and provincial veterinary associations, from April to June 2021. Responses were analyzed using descriptive statistics, univariable logistic regression, and content analysis. Results: rescue organizations (92%). Few reported imported dogs arriving with core vaccines or parasiticides administered. Despite infectious disease concerns, only 14% routinely implemented enhanced infection-control practices with imported dogs in clinics. Resources outlining country-specific disease risks, foreign disease screening, and client education were deemed highly valuable for supporting clinical practice. Conclusion and clinical relevance: Canadian veterinarians' experiences reinforced gaps in the healthcare of imported dogs, highlighted inconsistencies in clinical management of these dogs, and identified areas in which educational resources could improve animal health and the practice of importing dogs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".