A Randomized Trial Comparing Freely Moving and Zonal Instruction of Veterinary Surgical Skills Using Ovariohysterectomy Models
Bibliographic record
Abstract
= 76) surgical skills were assessed after training using either the traditional (T) method of large-group teaching by multiple instructors or the alternative method of one instructor assigned (A) to a defined group of students. Instructors rotated to a different group of students for each laboratory session. The instructor-to-student ratio and environment remained identical. No differences were found in raw assessment scores or the number of students requiring remediation, suggesting that students learned in this environment whether they received feedback from one instructor or multiple. Students had no preference between the methods, though 88% of the instructors preferred the assigned method, because they perceived an increased ability to teach and observe individual students. There was no difference in the number of students identified as at-risk of remediation between groups. When both groups were considered together, students identified as at-risk were more likely (40% vs. 10%) to require post-assessment remediation. However, only 22% of students requiring remediation had been identified as at-risk, and A-group instructors were more accurate than T-group instructors at identifying at-risk students. These results suggest that students accept either instructional method, but most instructors prefer to be assigned to a small group of students. Surgical skills were learned similarly well by students in both groups, although assigned instructors were more accurate at identifying at-risk students, which could prove beneficial if early intervention measures can be offered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".