When veterinarians treat plus-sized pets: Insights for veterinary practice.
Bibliographic record
Abstract
Background: Obesity of companion animals in Canada is becoming a problem in veterinary practice. Cats and dogs, in particular, are increasingly overweight. However, prevention and treatment present challenges. Challenges in treating pet obesity, such as client nonadherence and animal welfare issues arising from obesity, also affect the well-being of veterinarians - especially given the coincident high rates of burnout and compassion fatigue experienced in the profession. Objective: This study investigated how practicing veterinarians perceive the treatment of overweight companion animals and how treating obese pets affects well-being of veterinarians. Animals and procedure: We recruited veterinarians who routinely treat companion animals in private practice to participate in focus group interviews. We also interviewed veterinarians who could not attend the focus group sessions, yet still wished to contribute. Through thematic data analysis, we generated key themes that illustrated how treating obese pets negatively affects veterinarian well-being. Results: Eighteen companion animal veterinarians contributed to this study. We generated 3 themes from the analysis that illustrate negative effects of treating obese pets on veterinarian well-being: i) negative feelings such as frustration and sadness associated with treating obese pets; ii) owners' lack of comprehension of the effects of obesity on pets, including early euthanasia; and iii) client nonadherence regarding treatment. Conclusion and clinical relevance: This study contributes to the veterinary literature on companion animal obesity by focusing on how treating pet obesity affects veterinarian well-being, especially given high rates of burnout and compassion fatigue in the profession. As pet obesity increases in society, obesity prevention and treatment is becoming central to companion animal veterinary practice. Our findings suggest that veterinarian well-being is negatively affected in connection with companion animal obesity. We recommend relationship-centered communication, increased nutritional expertise, and a focus on wellness in the workplace to improve veterinarian well-being while treating pet obesity.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".