Strategies Used for Making Healthy Eating Choices among Veterinary Medical Students
Bibliographic record
Abstract
Healthy eating is a challenge for most college students, and students in the field of veterinary medicine are no exception. Health experts have recommended that universities emphasize the importance of healthy eating and promote healthy eating habits among students. However, before we can begin offering targeted interventions to promote healthy eating strategies and behaviors, we must first understand students' current strategies used for making healthy eating choices, self-reported eating habits, and perceptions of diet quality. Thus, the purpose of this study was to understand veterinary medical students' perceptions of current diet quality and to characterize their strategies for making healthy eating choices. Results indicate veterinary medical students employ a wide range of strategies and behaviors for healthy eating, yet few students reported eating a diet of poor quality. We conclude that while most students report eating a relatively healthy diet, variation in strategies used suggests room for improvement for many. This article discusses potential intervention strategies to promote healthy eating among veterinary students.
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How this classification was reachedexpand
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".