Validity Evidence for a Bovine Uterine Prolapse Reduction Model and Rubric for Use in Teaching and Low-Stakes Assessment of Veterinary Students
Bibliographic record
Abstract
Abstract Bovine uterine prolapse is a common but emergent condition typically arising in the time surrounding calving. Without treatment, it can result in tissue trauma, infection, hemorrhage, and death. Teaching veterinary students to perform uterine prolapse reduction has historically been dependent upon adequate clinical case load requiring the procedure. This study sought to develop and collect validation evidence for a silicone bovine uterine prolapse reduction model and associated scoring rubric to enable procedural practice without the presentation of live animals requiring the procedure. This study utilized a validation framework consisting of content evidence (expert opinion), internal structure evidence (reliability of scores produced by the rubric), and relationship with other variables evidence (level of training, novice-to-expert comparison). Veterinary students ( n = 37, novices) and veterinarians ( n = 11, experts) performed the procedure on the model while being video recorded. All participants then completed a survey about the model. Veterinarians’ survey results indicated that the model adequately represented the task and was suitable for teaching and assessing veterinary students’ skill in the procedure (content evidence). Scores produced by the rubric had a marginal Cronbach's alpha (.607), suggesting that the rubric may be adequate for low-stakes assessment but would require additional items or modification in order to improve reliability and be suitable for high-stakes assessment (internal structure evidence). Finally, experts achieved higher total rubric scores than novices did (relationship with other variables evidence). This study demonstrated content evidence and relationship with other variables evidence for the bovine uterine prolapse model, indicating its usefulness for teaching this important clinical skill.
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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 | 0.077 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".