Correlation between Student Self-Assessment and Proctor Evaluation in a Veterinary Surgical Laboratory
Bibliographic record
Abstract
Self-assessment has been shown to facilitate learning, goal setting, and professional development. We sought to evaluate whether veterinary students in a surgical curriculum would have self-assessments that differed from proctor evaluations and whether high-performing students would differ from low-performing students in self-assessment characteristics. Student and proctor assessments were compared for 8 weeks of a spay/neuter surgical laboratory taking place in the second year of the curriculum. Eight students were classified as high-performing, and 10 students were classified as low-performing. A quantitative evaluation of the scores and a qualitative assessment of written comments were completed. Proctors assigned higher scores to high-performing students compared to low-performing students, but no difference was observed overall in self-assessment scores assigned by students. When only anesthesia students were evaluated, we found a difference in self-assessment scores for high- versus low-performers, but this was not true for surgery students. Differences between proctor and student assessment scores diminished over time for all students and for anesthesia students, but not for surgery students. High-performing student anesthetists self-assessed and received proctor assessments with higher scores in technical skills. Comments from high-performing students tended to be less reflective and more positive. Low-performing students were more defensive and more likely to use I-statements in their comments. Overall, quantitative analysis did not reveal a difference in self-assessment between high-performers and low-performers; however, specific differences existed in qualitative characteristics, surgery versus anesthesia students, and proctor assessments. The differences in self-assessment between high- and low-performing students suggest areas of further investigation for the use of reflection in education.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".