The Demographics and Evaluation of the Externship Experiences Arranged by Michigan State University Veterinary Medicine Students
Bibliographic record
Abstract
RATIONALE FOR THE STUDY: The clinical curriculum at the Michigan State University (MSU) College of Veterinary Medicine encourages off-campus study. One elective, veterinary externship, allows students to arrange experiences that are expected to challenge them academically and provide realism about private practice and nontraditional employment. This study was undertaken to describe the demographics of student-arranged externships and to characterize the evaluation of our students by on-site mentors. METHODOLOGY: Externship demographics and evaluation data were analyzed for the participants from two consecutive veterinary classes. Mentors evaluated 11 categories relating to student knowledge, ability, enthusiasm, or potential and responded to three questions. RESULTS: 88% of students from the classes of 1999 and 2000 elected either one or two externships. Small animal practice settings predominated; externships were frequently used to gain experience with a particular species or discipline; and non-private practice settings accounted for 21% of the total. Statistical differences among the 11 evaluation categories are reported. CONCLUSIONS: Whether in traditional or nontraditional arenas, externships offer an effective way to give students choices in the attainment of veterinary skills, abilities, knowledge, and potential. Mentors were dedicated to their responsibilities and provided useful evaluations. In comparison to other evaluation categories, our students lacked an understanding of entry-level practice and business management concepts. Many mentors failed to equate practice and business management with the clinical education of 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".