Ontario Veterinary College First-Year Veterinary Students’ Perceptions of Companion Animal Nutrition and Their Own Nutrition: Implications for a Veterinary Nutrition Curriculum
Bibliographic record
Abstract
Extant research shows veterinarians face increasing challenges in discussing nutrition with clients despite receiving professional nutrition education in the veterinary medical curriculum. This article’s aim is to elicit student veterinarians’ baseline nutrition-related perceptions and nutrition information-seeking behaviors at the time of entering veterinary school. Participants were newly enrolled veterinary students at the Ontario Veterinary College ( n = 120). Focus group discussions ( n = 19) informed the design of an online questionnaire capturing students’ demographics and perceptions of their own and their pets’ nutrition. Students reported being influenced by individual factors (e.g., time), social networks (e.g., family), and surrounding environment (e.g., cost, contradictory media messages). Overall, 58% of students considered themselves knowledgeable about pet nutrition when commencing veterinary school, with 71% prioritizing their pets’ diets as much as their own. Students’ confidence in finding pet nutrition information was correlated with perceived accessibility ( r = .76, p = .001) and perceived quantity of information available on pet nutrition ( r = .83, p = .001), but not quality of information ( r = .13, p = .03). In general, students relied on and trusted veterinarians for nutrition advice. However, 94% of students mistrusted pet food companies’ motivations. Our data support that students entering veterinary school have their own perceptions on pet nutrition that impact nutrition education, suggesting this as an important consideration in the design and delivery of a veterinary nutrition curriculum. Veterinary medical faculty should be encouraged to discuss baseline nutrition information and address any misconceptions to prepare students for future consultations with clients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".