Assessing the Impact of a Pilot Nutrition Curriculum on Students’ Confidence and Ability to Perform Nutritional Assessments on Overweight Dogs and Cats for Use in a Veterinary Outreach Program
Bibliographic record
Abstract
Obesity is a growing concern for dogs and cats. Although veterinary input is critical to prevent and manage obesity, conversations addressing overweight pets are challenging and require training to perform effectively. This study assessed the impact of a nutrition curriculum developed for use in a veterinary outreach program on student confidence and ability to perform nutritional assessments, particularly on overweight pets. The curriculum was developed by students and a Board-Certified Veterinary Nutritionist focusing on (1) performing nutritional assessments and (2) discussing the findings with owners. Initial implementation and evaluation occurred with 32 students. Pre-study and post-study surveys were conducted asking students to rank their confidence in 14 aspects related to nutritional assessments, determine opportunities for change from a case summary, and describe their experience using the materials. Five students in the outreach program performed an additional nutritional assessment and developed a plan for a hypothetical case. Results were analyzed for significance via the likelihood ratios Chi-square and Wilcoxon signed-rank tests. Students showed significant increase in confidence for 11 of the 14 questions and significant improvements at determining opportunities for change ( p < .05). Feedback was positive and supported the feasibility of using the materials with the outreach program. Overall, the findings support that the curriculum provides a positive learning experience and prepared veterinary students for performing nutritional assessments and creating management plans for obese pets. This article introduces the curriculum as a successful model for providing access to additional self-paced curricular units to veterinary students.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".