Feline Obesity in Veterinary Medicine: Insights from a Thematic Analysis of Communication in Practice
Bibliographic record
Abstract
Feline obesity has become a common disease and important animal welfare issue. Little is known about how, or how often, veterinarians and feline-owning clients are addressing obesity during clinical appointments. The purpose of this qualitative study was to characterize verbal and non-verbal communication between veterinarians and clients regarding feline obesity. The sample consisted of video-recordings of 17 veterinarians during 284 actual appointments in companion animal patients in Eastern Ontario. This audio-visual dataset served to identify 123 feline appointments. Of these, only 25 appointments were identified in which 12 veterinarians and their clients spoke about feline obesity. Thematic analysis of the videos and transcripts revealed inconsistencies in the depth of address of feline obesity and its prevention by participating veterinarians. In particular, in-depth nutritional history taking and clear recommendations of management rarely took place. Veterinarians appeared to attempt to strengthen the veterinary-client relationship and cope with ambiguity in their role managing obesity with humor and by speaking directly to their animal patients. Clients also appeared to use humor to deal with discomfort surrounding the topic. Our findings have implications for communication skills training within veterinary curricula and professional development among practicing veterinarians. As obesity is complex and potentially sensitive subject matter, we suggest a need for veterinarians to have further intentionality and training toward in-depth nutritional history gathering and information sharing while navigating obesity management discussions to more completely address client perspective and patient needs.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".