Can a Simple Model Have Value Without Validation? A Study to Develop and (Attempt to) Validate a Bovine Caudal Epidural Model and Rubric
Bibliographic record
Abstract
Bovine practitioners expect new graduates entering clinical practice to be able to place a caudal epidural. Teaching this task on models facilitates scheduled training sessions and sufficient practice to reach competency. This study sought to create and validate a bovine caudal epidural model and scoring rubric using a framework of content evidence, internal structure evidence, and relationship with other variables evidence. Veterinarians ( n = 11) and students ( n = 40) were video recorded while placing a caudal epidural on the model. Recordings were scored by a blinded rater. Participants completed a survey evaluating the model's features, ease of use, and anticipated best use. Veterinarians reported that the model was helpful for students to learn and practice the task and that the model had sufficient landmark features and realism ( content evidence). Rubric scores achieved acceptable internal consistency after one item was dropped (α = .736; internal structure evidence), and there was no significant difference between veterinarians’ and students’ performance scores on the model ( relationship with other variables evidence). Survey feedback indicated the task on the model was simple, allowing students to achieve scores similar to those of veterinarians. Therefore, the model and rubric were not able to be validated using this study's validity framework. However, there are simple clinical skills models used in veterinary education and other health care fields, and research suggests that learning does take place on these models. Educators must consider whether simple models that are helpful for students to practice their skills may still have value, even if they are not able to be validated.
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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 | 0.110 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".