The Use of Global Rating Scales for OSCEs in Veterinary Medicine
Bibliographic record
Abstract
OSCEs (Objective Structured Clinical Examinations) are widely used in health professions to assess clinical skills competence. Raters use standardized binary checklists (CL) or multi-dimensional global rating scales (GRS) to score candidates performing specific tasks. This study assessed the reliability of CL and GRS scores in the assessment of veterinary students, and is the first study to demonstrate the reliability of GRS within veterinary medical education. Twelve raters from two different schools (6 from University of Calgary [UCVM] and 6 from Royal (Dick) School of Veterinary Studies [R(D)SVS] were asked to score 12 students (6 from each school). All raters assessed all students (video recordings) during 4 OSCE stations (bovine haltering, gowning and gloving, equine bandaging and skin suturing). Raters scored students using a CL, followed by the GRS. Novice raters (6 R(D)SVS) were assessed independently of expert raters (6 UCVM). Generalizability theory (G theory), analysis of variance (ANOVA) and t-tests were used to determine the reliability of rater scores, assess any between school differences (by student, by rater), and determine if there were differences between CL and GRS scores. There was no significant difference in rater performance with use of the CL or the GRS. Scores from the CL were significantly higher than scores from the GRS. The reliability of checklist scores were .42 and .76 for novice and expert raters respectively. The reliability of the global rating scale scores were .7 and .86 for novice and expert raters respectively. A decision study (D-study) showed that once trained using CL, GRS could be utilized to reliably score clinical skills in veterinary medicine with both novice and experienced raters.
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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 | 0.044 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".