Practice-Based Education at Michigan State University
Bibliographic record
Abstract
Practice-based large animal education at Michigan State University (MSU) was initiated in 1995. Urbanization of the area surrounding MSU and the declining number of livestock and farms to provide clinical experience under field conditions were the major reasons for adopting this educational model. Veterinary students, as a requirement for graduation from the professional program, must successfully complete a three-week clerkship in a private practice that has either a food animal or an equine emphasis. The objective of the practice-based program is to provide students with introductory on-the-farm experience, with emphasis on examination, diagnosis, treatment, and prevention of common medical and surgical conditions of horses and/or food animals. Participating practices must be located in Michigan, have a sufficient case load, be interested in teaching senior veterinary students, and be willing to interact with the program coordinator and attend annual meetings. Sixty-nine practices are currently participating. The program coordinator matches students to practices based on the students' species interest and background, the personalities of the student and practitioner, and any special concerns or needs of the students (health conditions, child care). Assessment of students includes a practitioner's performance evaluation and review of written assignments (case log, clinical case reports, and a practice management assessment report) by campus faculty. A pass/no pass grading system is used. Overall, the Practice-Based Ambulatory Program represents a successful model for meeting the clerkship objectives. Additionally, exposing students to a high volume of commonly encountered conditions in a private business setting complements the referral cases seen in our large animal teaching hospital.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.015 |
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".