A survey of animal welfare experts and practicing veterinarians to identify and explore key factors thought to influence canine and feline welfare in relation to veterinary care
Bibliographic record
Abstract
Abstract Veterinary care is important for maintaining companion animal health; however, it also has the potential to impact other aspects of patient welfare. To investigate factors related to veterinary care that are likely to influence canine and feline welfare, animal welfare researchers, veterinarians with an expertise in animal welfare, and Canadian and American companion and mixed animal veterinarians were invited to participate in a three-stage online survey. Participants were asked to do the following: i) identify factors related to the veterinary experience that impact patient welfare; ii) rate the relative impact of each factor; and iii) gauge the feasibility of measuring and improving each factor. Overall, 78 participants identified 85 factors that impact animal welfare in the clinic (eg restraint techniques) and home environment (eg advice regarding behaviour and training). Among factors, seven themes emerged: physical environment of the clinic; routine animal care provided by veterinary team members (‘staff); interactions between the patient, staff, and client; clinic management; medical and surgical procedures; staff attitudes and education; and communication between the veterinarian and client. Mean relative impact scores ranged from 1.0 to 3.8 on a five-point scale (0-4), with 70% of factors receiving a score greater than 3. Most participants (> 80%) agreed that 68% of the identified factors could be feasibly improved in an average veterinary clinic and that 43% of the factors could be feasibly measured during a welfare assessment. These results identify key areas where veterinary care may impact the welfare of canine and feline patients and highlight priority areas where assessment and improvement are possible.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".