Video Recording in Veterinary Medicine OSCEs: Feasibility and Inter-rater Agreement between Live Performance Examiners and Video Recording Reviewing Examiners
Bibliographic record
Abstract
The Objective Structured Clinical Examination (OSCE) is a valid, reliable assessment of veterinary students’ clinical skills that requires significant examiner training and scoring time. This article seeks to investigate the utility of implementing video recording by scoring OSCEs in real-time using live examiners, and afterwards using video examiners from within and outside the learners’ home institution. Using checklists, learners (n=33) were assessed by one live examiner and five video examiners on three OSCE stations: suturing, arthrocentesis, and thoracocentesis. When stations were considered collectively, there was no difference between pass/fail outcome between live and video examiners (χ2 = 0.37, p = .55). However, when considered individually, stations (χ2 = 16.64, p < .001) and interaction between station and type of examiner (χ2 = 7.13, p = .03) demonstrated a significant effect on pass/fail outcome. Specifically, learners being assessed on suturing with a video examiner had increased odds of passing the station as compared with their arthrocentesis or thoracocentesis stations. Internal consistency was fair to moderate (0.34–0.45). Inter-rater reliability measures varied but were mostly moderate to strong (0.56–0.82). Video examiners spent longer assessing learners than live raters (mean of 21 min/learner vs. 13 min/learner). Station-specific differences among video examiners may be due to intermittent visibility issues during video capture. Overall, video recording learner performances appears reliable and feasible, although there were time, cost, and technical issues that may limit its routine use.
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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 | 0.089 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".