Veterinary Student Self-Assessment of Basic Surgical Skills as an Experiential Learning Tool
Bibliographic record
Abstract
Mastery of basic skills is critical for surgical training. Such training is best obtained by experiential learning, which requires an element of self-reflection. Self-reflection is not always an automatic process, however; guidance may be required. This article sought to determine whether guided self-assessment would help facilitate student mastery of learned skills in a veterinary basic surgery course. The course consisted of 18 lectures and eight laboratories. Students were provided with written notes and presentation slides before the course. At the end of each lab, students completed a self-assessment of their skills. Skills were practiced in multiple labs; at the end of the course, each student was given a graded, practical examination to evaluate skills mastery. Statistical analysis was performed to compare students' mean self-assessment over the eight labs and to determine whether self-assessment scores correlated with examination grades. Results from 80 students were included. Students' overall self-assessments improved significantly from lab 1 to lab 8, and their self-assessment of two specific skills (closed gloving and simple continuous suture pattern) also improved. Students' self-assessments after the eighth lab were predictive of their practical exam scores. These results suggest guided reflection in the form of self-assessment could help facilitate student mastery of basic surgery skills. Correlation between self-assessment and practical examination results suggests instructors may use these self-assessments to detect students who need extra practice or instruction.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".