Development of a Simple, Rapid, Convenience Sampling Method to Evaluate the Validity of Clinical Skills Models and Protocols in a Veterinary Educational Setting
Bibliographic record
Abstract
The use of clinical skills models is now commonplace in veterinary education, with the aim of improving proficiency and competency when subsequently performing clinical procedures on patients. However, it is important to evaluate the construct and content validity of the models and protocols being used to replace live animal teaching. Performing in-depth validation studies takes considerable time and resources, which may not be readily available in an educational setting. This study describes a fast and effective method using expert feedback to evaluate the validity of clinical skills models and their associated protocols used in veterinary teaching. A total of 30 skills used in the teaching of undergraduate veterinary students at the University of Surrey (UK) were evaluated, 10 from each of the core species (companion animal, equine, and production animal). Qualified veterinary surgeons with experience performing each skill were invited to read through the protocol and perform the skills. They were then asked to provide anonymous ratings using a 5-point Likert scale regarding: the realism of the model, the suitability of the protocol, and the suitability of the model and protocol to prepare students to perform the skill in clinical practice. The results showed that 80% of respondents agreed that performing the skill was realistic compared with the live animal for 63.3% of skills, that the written protocol was appropriate for performing this skill for 96.7% of skills, and/or that the model and protocol were suitable to prepare students to perform the skill in clinical practice for 76.7% of skills. This study presents an innovative approach to high-throughput clinical skills teaching validation.
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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 | 0.120 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".