Effects of changing veterinary handling techniques on canine behaviour and physiology Part 2: Behavioural measurements
Bibliographic record
Abstract
Abstract Signs of distress in dogs during veterinary visits are often normalised rather than viewed as welfare concerns. Interventions designed to reduce fear during veterinary visits were evaluated to see if they affected dogs’ behaviours compared to dogs without interventions. Twenty-eight dogs were examined at four visits across eight weeks. Dogs were randomised into intervention (distress reduction/adaptive care) and control groups (standard care) and evaluated via the Working Dog Questionnaire – Pet Dog Version (WDQ-Pet). At visit 1 (baseline) all dogs received the control protocol. Homework was assigned following visit 1 to practice collaborative examination (intervention) or to pet the dog (control) for the same allotted time. At each visit, behaviours were scored (clinical stress score) via video and in-person observations when dogs entered the hospital, stepped onto a scale to be weighed, entered the exam room, at the beginning and end of examination, and after venipuncture. There were no differences between groups at visit 1, or across visits entering the hospital or exam room. At visit 4, intervention scores either decreased or remained low when weighed, and at the beginning and end of the physical exam. Control scores were significantly higher than the intervention scores during these periods. Reduced clinical stress scores indicate intervention dogs had improved care experience compared to the control. The study results highlight the value of applying simple and adaptable interventions, ultimately leading to improved animal care and welfare.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".