Veterinary professionals’ weight-related communication when discussing an overweight or obese pet with a client
Bibliographic record
Abstract
OBJECTIVE: Pet weight may be difficult for veterinary professionals to address with clients, particularly when pets are overweight or obese. The objective of this study was to characterize the communication processes and content of weight-related conversations occurring between veterinary professionals and clients. SAMPLE: Audio-video recordings of 917 veterinarian-client-patient interactions involving a random sample of 60 veterinarians and a convenience sample of clients. PROCEDURES: Companion animal veterinarians in southern Ontario, Canada, were randomly recruited, and interactions with their clients were audio-video recorded. Interactions were reviewed for mentions of weight, then further analyzed by means of a researcher-generated coding framework to provide a comprehensive assessment of communication specific to weight-related interactions. RESULTS: 463 of 917 (50.5%) veterinary-client-patient interactions contained an exchange involving the mention of a single patient's (dog or cat) weight and were included in final analysis. Of the 463 interactions, 150 (32.4%) involved a discussion of obesity for a single patient. Of these, 43.3% (65/150) included a weight management recommendation from the veterinary team, and 28% (42/150) provided clients with a reason for pursuing weight management. CLINICAL RELEVANCE: Findings illustrate opportunities to optimize obesity communication to improve the health and wellbeing of veterinary patients.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".