Comparison of Two Clinical Teaching Models for Veterinary Emergency and Critical Care Instruction
Bibliographic record
Abstract
Standards to oversee the implementation and assessment of clinical teaching of emergency and critical care for veterinary students do not exist. The purpose of this study was to assess differences in the learning environment between two veterinary emergency and critical care clinical rotations (one required, one elective) with respect to caseload, technical/procedural opportunities, direct faculty contact time, client communication opportunities, and students' perception of practice readiness. The authors designed a 22-item survey to assess differences in the learning environment between the two rotations. It was sent electronically to 35 third- and fourth-year veterinary medicine students. Bivariate analysis, including the Wilcoxon signed-rank test and the t-test, were used to compare differences between pre-test and post-test scores among students. Twenty-six students' responses were included from the required rotation and nine from the elective rotation. Findings showed that students preferred the elective community emergency department setting to the required academic setting and that there were statistically significantly more positive experiences related to the variables of interest. Students saw significantly more cases at the community emergency department setting. Findings from this study offer guidance to assess students' emergency department rotations, suggest how teaching interactions can be modified for optimal learning experiences, and ensure that students receive maximal opportunities to treat patients that are representative of what they would encounter in practice.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".