Development and Validation of an Equine Castration Model and Rubric
Bibliographic record
Abstract
Castration is one of the most common surgeries performed in equine practice. Veterinary students require deliberate practice to reach competence in surgical procedures including equine castration, but availability of patients limits students’ practice opportunities. A recumbent equine castration model was created and evaluated using a validation framework consisting of content evidence (expert opinion), internal structure evidence (reliability of scores produced by the accompanying rubric), and evidence of relationship with other variables, specifically the difference in scores between experts and students. A convenience sample of third-year students who had never performed equine castration ( n = 24) and veterinarians who had performed equine castration ( n = 25) performed surgery on the model while being video recorded. Participants completed a post-operative survey about the model. All veterinarians (100%) agreed or strongly agreed that the model was suitable for teaching students the steps to perform equine castration and for assessing students’ skill. The checklist produced scores with good internal consistency ( α = 0.805). Veterinarians performed the castration faster than the students ( p = .036) and achieved a higher total global rating score ( p = .003). There was no significant difference between groups in total checklist score or individual checklist items, except veterinarians were more likely to check both sides for bleeding ( p = .038). The equine castration model and rubric validated in this study can be used in a low-stress clinical skills environment to improve students’ skills to perform what is otherwise a challenging field procedure. Model use should be followed with live animal practice to complete the learning process.
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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 | 0.046 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".