Ontario Veterinary College First-Year Veterinary Students’ Perceptions of Companion Animal Nutrition and Their Own Nutrition: Implications for a Veterinary Nutrition Curriculum
Why this work is in the frame
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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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it