Veterinary Intern Program for Entrustable Professional Activities Skills, a.k.a. Intern Boot Camp
Bibliographic record
Abstract
The University of Tennessee College of Veterinary Medicine (UTCVM) Department of Large Animal Clinical Sciences has developed an intensive training program that all large animal veterinary interns are required to complete at the onset of their internship year. This program was designed to establish a uniform approach to entrustable professional activity (EPA) skills deemed essential by the large animal faculty. These EPA skills emphasize the clinical approaches and skills that interns should understand and demonstrate competency in early in their internship year. The EPA program, completed over 4 consecutive days, was coined the “Intern Boot Camp” and structured to fuse case-based lecture discussions and hands-on wet labs designed to develop or improve skills and techniques. At the conclusion of the boot camp, participants were given an evaluation survey to provide feedback on the program. The results were overwhelmingly positive, with 90% of the participants giving the program a rating of 5 on a scale ranging from 1 ( poor opinion or experience) to 5 ( excellent opinion or experience). Nearly 95% of participants stated that they felt more prepared for their internship year after attending the boot camp, and 100% of the participants indicated that they would recommend this program to future candidates. Given the positive outcomes over the past 4 years, the implementation of the Veterinary Intern Entrustable Professional Activities program (Intern Boot Camp) is considered a valuable component of the veterinary intern training program and could readily be adapted to other programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".