Teaching and Assessing Nutrition Competence in a Changing Curricular Environment
Bibliographic record
Abstract
In the past, the required introductory veterinary nutrition course at Michigan State University's College of Veterinary Medicine (MSU-CVM) has provided 29 hours of didactic lectures, with student performance evaluated by short-answer or multiple-choice questions. Because of a 50% reduction in allotted course credits and a change in prerequisites for admission, the course is being redesigned to focus on three of 29 nutrition competencies outlined by the American College of Veterinary Nutrition. Professional communication skills will be developed through small-group learning experiences, case-based problems, and videotaped interviews with standardized clients to teach and assess nutrition competencies. Assessment strategies will differ from traditional multiple-choice examinations and include pre- and post-course self-efficacy ratings, written evaluations from trained standardized clients, and oral and written evaluations from coaches or facilitators.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".