Validation of a Rubric Used for Skills-Based Assessment of Veterinary Students Performing Simulated Ovariohysterectomy on a Model
Bibliographic record
Abstract
Abstract Surgical skills are an important competency for new graduates. Simulators offer a means to train and assess veterinary students prior to their first surgical performance. A simulated ovariohysterectomy (OVH) rubric’s validity was evaluated using a framework of content evidence, internal structure evidence, and evidence of relationship with other variables, specifically subsequent live surgical performance. Clinically experienced veterinarians ( n = 13) evaluated the utility of each rubric item to collect evidence; each item’s content validity index was calculated to determine its inclusion in the final rubric. After skills training, veterinary students ( n = 57) were assessed using the OVH model rubric in March and August. Internal structure evidence was collected by video-recording 14 students’ mock surgeries, each assessed by all five raters to calculate inter-rater reliability. Evidence of relationship with other variables was collected by assessing 22 students performing their first live canine OVH in November. Experienced veterinarians included 22 items in the final rubric. The rubric generated scores with good to excellent internal consistency; inter-rater reliability was fair. Students’ performance on the March model assessment was moderately correlated with their live surgical performance ( ρ = 0.43) and moderately negatively correlated with their live surgical time ( ρ = −0.42). Students’ performance on the August model assessment, after a summer without surgical skills practice, was weakly correlated with their live surgical performance ( ρ = 0.17). These data support validation of the simulated OVH rubric. The continued development of validated assessment instruments is critical as veterinary medicine seeks to become competency based.
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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 | 0.054 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".