Standardised clients as assessors in a veterinary communication OSCE: a reliability and validity study
Bibliographic record
Abstract
In human medicine, standardised patients (SP) have been shown to reliably and accurately assess learners' communication performance in high-stakes certification Objective Structured Clinical Examinations (OSCE), offering a feasible way to reduce the need for recruitment, time commitment and coordination of faculty assessors. In this study, we evaluated the use of standardised clients (SC) as a viable option for assessing veterinary students' communication performance. We designed a four-station, two-track communication skills OSCE. SC assessors used an adapted nine-item Liverpool Undergraduate Communication Assessment Scale (LUCAS). Faculty used a 21-item checklist derived from the Calgary-Cambridge Guide (CCG) and a five-point global rating scale. Participants were second year veterinary students (n=96). For the four stations, intrastation reliability (α) ranged from 0.63 to 0.82 for the LUCAS, and 0.73 to 0.87 for the CCG. The interstation reliability coefficients were 0.85 for the LUCAS and 0.89 for the CGG. The calculated Generalisability (G) coefficients were 0.62 for the LUCAS and 0.60 for the CGG. Supporting construct validity, SC and faculty assessors showed a significant correlation between the LUCAS and CCG total percent scores (r=0.45, P<0.001), and likewise between the LUCAS and global rating scores (r=0.49, P<0.001).Study results support that SC assessors offer a reliable and valid approach for assessing veterinary communication OSCE.
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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 | 0.066 | 0.122 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".