Assessment of First-Year Veterinary Students' Clinical Skills Using Objective Structured Clinical Examinations
Bibliographic record
Abstract
The DVM program at the University of Calgary offers a Clinical Skills course each year for the first three years. The course is designed to teach students the procedural skills required for entry-level general veterinary practice. Objective Structured Clinical Examinations (OSCEs) were used to assess students' performance on these procedural skills. A series of three OSCEs were developed for the first year. Content was determined by an exam blueprint, exam scoring sheets were created, rater training was provided, a mock OSCE was performed with faculty and staff, and the criterion-referencing Ebel method was used to set cut scores for each station using two content experts. Each station and the overall exam were graded as pass or fail. Thirty first-year DVM students were assessed. Content validity was ensured by the exam blueprint and expert review. Reliability (coefficient α) of the stations from the three OSCE exams ranged from 0.0 to 0.71. The three exam reliabilities (Generalizability Theory) were, for OSCE 1, G=0.56; OSCE 2, G=0.37; and OSCE 3, G=0.32. Preliminary analysis has suggested that the OSCEs demonstrate face and content validity, and certain stations demonstrated adequate reliability. Overall exam reliability was low, which reflects issues with first-time exam delivery. Because this year was the first that this course was taught and this exam format was used, work continues in the program on the teaching of the procedural skills and the development and revision of OSCE stations and scoring checklists.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".