Dog Owner Perceptions of Veterinary Handling Techniques
Bibliographic record
Abstract
Veterinary care can be a source of stress for domestic dogs and their owners. If a dog owner is not satisfied with the veterinary experience, this may reduce the frequency of veterinary visits and negatively impact a dog’s health and welfare. Allowing dog owners to offer their perspectives on aspects of the veterinary appointment may help improve owner satisfaction. We assessed owner agreement towards 13 recommended handling techniques used on dogs during routine veterinary appointments, when the participants’ dog was calm, fearful, or aggressive. An online cross-sectional survey targeting current dog owners, residing in Canada and the United States, was used to examine the influence of participant’s pet attachment (using the Lexington Attachment to Pets Scale (LAPS)) and demographic information (age, gender, experience working in the veterinary field) on owner agreement towards the handling techniques. The majority of participants (N = 1176) disagreed with higher restraint techniques (e.g., full body restraint, muzzle hold) and tools (e.g., dog mask), and agreed with lower restraint techniques (e.g., minimal restraint) regardless of dog demeanor. Logistic regression models revealed that for medium/large dog owners, having previous veterinary work experience resulted in lower agreement with the use of minimal restraint (p < 0.0001) and higher agreement with the use of full body restraint on fearful dogs (p = 0.01). Small dog owners were more likely to agree with the use of minimal restraint on fearful dogs if they had a higher pet attachment score (p < 0.001), and were more likely to agree with full body restraint if they had previous veterinary work experience (p < 0.0001) or were male (p = 0.02). Owner perspectives align with current handling recommendations and provide further support for the use of low stress handling methods to improve owner satisfaction and dog welfare during routine veterinary care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".