Development and Validation of a Bovine Coccygeal Venipuncture Model and Rubric
Bibliographic record
Abstract
Abstract Diagnostic sample collection, including venipuncture, is critical to diagnosing and treating cattle. Clinical skills models permit learners to practice a skill and improve their competency before performing the skill on a live animal; however, relatively few bovine models exist. This study aimed to develop and validate a bovine coccygeal venipuncture model and rubric for teaching and assessing veterinary students using a validation framework consisting of content evidence, internal structure evidence, and relationship with other variables evidence. Veterinary students ( n = 38) and experienced veterinarians ( n = 12) performed venipuncture on the model while being video recorded. Recordings were scored blindly using a six-item rubric and a global rating score. Time to perform the task and total number of needle sticks were recorded. Veterinarians reported that the model was suitably realistic for students to learn to perform the task ( content evidence ). Rubric scores had acceptable reliability ( a = .783, internal structure evidence ). Veterinarians received higher rubric scores and used fewer needle sticks to complete the task ( p = .033 and .047, relationship with other variables evidence—level of training ). Students’ survey responses were very positive. The evidence collected in this study supported validation of the model and rubric. The use of validated models and rubrics allows educators to teach and assess skills reliably, and the model allowed students to practice the skill repetitively, reducing the use of live animals. Additional studies would be necessary to evaluate the model for use in teaching veterinary technicians, extension agents, and livestock producers to perform this task.
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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 | 0.040 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".