Perceptions of Small Animal Nutrition: An Exploration of Education and Self-Reported Proficiency Among Student Veterinary Professionals
Bibliographic record
Abstract
Nutrition is an essential knowledge area for student veterinary professionals, 1 , 2 yet commonly cited as an underrepresented topic in veterinary and veterinary nurse curricula. 3 – 5 Consequently, veterinary professionals may lack the knowledge, skills, or confidence to counsel clients and provide nutrition-related patient care. 6 – 8 This study forms the baseline stage of a longitudinal project comparing the nutrition knowledge and competence of first-year veterinary (VS) and veterinary nursing students (VNS) in the UK and Ireland. Participants were recruited by nonprobability, convenience purposive sampling, and by email invitation from educational providers. Data were collected between October 2023 and January 2024. Participation was voluntary and informed consent obtained. 135 VS and 186 VNS completed the online survey. Most (82%, n = 211) expressed interest in learning about nutrition. The ability to educate owners and assess pets’ physical condition and nutrition status was considered important by 97% ( n = 250) and 98% ( n = 253), respectively. Over three-quarters of respondents (77%; n = 178) believed that the diet should be evaluated and discussed at every veterinary visit. Students had greater confidence in their ability to conduct a nutritional assessment on dogs and cats than exotic pets. Fewer students (36%; n = 84) believed cooked diets to be healthier than raw, and perceived risks of raw outweighed benefits (38%; n = 88). Half of respondents were unsure about diet choices. Most VS (77%; n = 67) and VNS (87%; n = 125) deemed vegetarian diets unsuitable for dogs and cats. Students enter their studies with preconceived ideas and potential misinformation about nutrition. Nutrition education must be adequately represented within curricula to protect animal health.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".