Exploring veterinary professionals’ perceptions of pet weight‐related communication in companion animal veterinary practice
Bibliographic record
Abstract
BACKGROUND: Pet obesity is commonly encountered by veterinary professionals, yet little is known about their perception of communicating about pet weight. The objective of this study was to explore veterinary professionals' perception of discussing pet obesity with clients. METHODS: An online survey targeting veterinary professionals was distributed via social media and veterinary organisation newsletters. Topics included respondents' perceptions of weight-related communication, factors related to approaching weight conversations and implicit weight bias. RESULTS: A total of 102 respondents to the survey were included in the final analysis. Avoidance of discussing pet obesity with certain clients was common (53.9%; 55/102). The most endorsed term for describing pets with excess weight to clients was 'overweight' (97.1%; 99/102). The pet's body condition score was rated the most important factor to consider when deciding how to approach a weight discussion with clients. Although only 29 participants completed the implicit association test (IAT), most of these participants were identified as having an unconscious preference for thin people. The small sample size limited the vignette analysis to descriptive only, and the IAT results should be interpreted cautiously. CONCLUSION: This exploratory, cross-sectional study provides early insight into veterinary professionals' perceptions of pet obesity-related communication and suggests the presence of weight bias in the profession that warrants further investigation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".