Qualitative Pilot Study of Veterinarians' Perceptions of and Experiences with Counseling about Dog Walking in Companion-Animal Practice in Southern Ontario
Bibliographic record
Abstract
Dog walking is beneficial to dogs and their owners, and it supports One Health, an initiative devoted to improving the health of animals and humans. Despite the benefits of dog walking, many dogs and their owners are not engaging in adequate dog-walking exercise. One way to encourage dog walking is for veterinarians to counsel owners on dog walking during veterinary appointments. This pilot study used individual in-person or telephone interviews to understand veterinarians' perceptions of and experiences with counseling about dog walking in companion animal practice. Seventeen veterinarians licensed to provide canine care in Ontario participated in an interview in 2015. Thematic analysis was used to analyze the interview transcripts. Participants thought dog walking was important for both dogs and owners and discussed physical, social, and mental benefits of dog walking. Participants described barriers to dog-walking counseling including dog owners' lack of compliance or physical condition, dogs' behavior, and lack of time or low priority during appointments. Also, participants discussed how dog walking was not addressed in their professional veterinary education. These findings can be used to educate veterinary students and practicing veterinarians on the value of dog walking for people and dogs and the barriers they may face when counseling clients about dog walking.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".