Development and validation of a feline abdominal palpation model and scoring rubric
Bibliographic record
Abstract
Simulation in veterinary education enables clinical skills practice without animal use. A feline abdominal palpation model was created that allows practice in this fractious species. This study assessed the model and rubric using a validation framework of content evidence, internal structure and relationship with level of training. Content Evidence: Veterinarians accepted this model as a helpful training tool for students (median=4 on five-point Likert scale). Internal Structure Evidence: G-coefficients were low for first- and second-year students (0.28 and 0.23), but were acceptable for veterinarians (0.61). Internal consistency values (0.24, 0.42 and 0.67) followed a similar pattern. Thus, scores were more reliable for veterinarians than for the students. Evidence of Relationship with Level of Training: Although level of training impacted reliability, its effect on performance scores was inconsistent. Analysis of variance (ANOVA) identified no differences among the groups of students and veterinarians. However, effect size between first- and third-year students was medium to large (0.62). Effect sizes between the veterinarians and student groups were small. Although the model and rubric appeared valid for experts, modifications would be necessary to generate reliable scores for students. These results allow greater understanding of the needs of students utilising a low-fidelity model.
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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 | 0.048 | 0.072 |
| 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 | 0.001 |
| 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".