Perception of Canine Welfare Concerns among Veterinary Students, Practitioners, and Behavior Specialists in Spain
Bibliographic record
Abstract
Veterinarians are well placed to supervise and ensure canine welfare. However, the perception of animal welfare among veterinarians may vary depending on the level of training and professional practice, including the specialization in animal behavior and welfare. The aim of this study was to survey the perception of canine welfare among veterinarians, including students, practitioners, and behavior specialists. A scale-based questionnaire including 12 issues affecting canine welfare was adapted from Yeates and Main and distributed to first-year (n=50) and fifth-year veterinary students (n=50), as well as veterinary practitioners (n=260) and specialists in behavioral medicine (n=50). For each issue, respondents were asked to rate how much they perceived each issue to affect canine welfare (on a scale of 0 to 4). A General Linear Model test was used to assess the effect of the studied group on scores. "Physical abuse or cruelty" was the highest-scoring problem in all groups and "breed-related conditions" was the lowest. In general, specialists in behavioral medicine assigned significantly higher scores to most items, particularly "behavioral problems" and "lack of sufficient company." In contrast, fifth-year students assigned significantly lower scores to most items. This study shows that situations clearly affecting canine welfare represent an important concern for veterinarians, both undergraduates and professionals. However, the level of professional experience and specialization might influence the perception of more subtle examples of poor welfare. Raising awareness regarding canine welfare, including concern for breed- or behavior-related problems, should be emphasized within university programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".