Evaluating validity evidence for 2 instruments developed to assess students' surgical skills in a simulated environment
Bibliographic record
Abstract
OBJECTIVE: To gather and evaluate validity evidence in the form of content and reliability of scores produced by 2 surgical skills assessment instruments, 1) a checklist, and 2) a modified form of the Objective Structured Assessment of Technical Skills (OSATS) global rating scale (GRS). STUDY DESIGN: Prospective randomized blinded study. SAMPLE POPULATION: Veterinary surgical skills educators (n =10) evaluated content validity. Scores from students in their third preclinical year of veterinary school (n = 16) were used to assess reliability. METHODS: Content validity was assessed using Lawshe's method to calculate the Content Validity Index (CVI) for the checklist and modified OSATS GRS. The importance and relevance of each item was determined in relation to skills needed to successfully perform supervised surgical procedures. The reliability of scores produced by both instruments was determined using generalizability (G) theory. RESULTS: Based on the results of the content validation, 39 of 40 checklist items were included. The 39-item checklist CVI was 0.81. One of the 6 OSATS GRS items was included. The 1-item GRS CVI was 0.80. The G-coefficients for the 40-item checklist and 6-item GRS were 0.85 and 0.79, respectively. CONCLUSION: Content validity was very good for the 39-item checklist and good for the 1-item OSATS GRS. The reliability of scores from both instruments was acceptable for a moderate stakes examination. IMPACT: These results provide evidence to support the use of the checklist described and a modified 1-item OSAT GRS in moderate stakes examinations when evaluating preclinical third-year veterinary students' technical surgical skills on low-fidelity models.
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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 | 0.244 | 0.592 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".