Practical Tips for Setting Up and Running OSCEs
Bibliographic record
Abstract
Objective structured clinical examinations (OSCEs) are used to assess students' skills on a variety of tasks using live animals, models, cadaver tissue, and simulated clients. OSCEs can be used to provide formative feedback, or they can be summative, impacting progression decisions. OSCEs can also drive student motivation to engage with clinical skill development and mastery in preparation for clinical placements and rotations. This teaching tip discusses top tips for running an OSCE for veterinary and veterinary nursing/technician students as written by an international group of authors experienced with running OSCEs at a diverse set of institutions. These tips include tasks to perform prior to the OSCE, on the day of the examination, and after the examination and provide a comprehensive review of the requirements that OSCEs place on faculty, staff, students, facilities, and animals. These tips are meant to assist those who are already running OSCEs and wish to reassess their existing OSCE processes or intend to increase the number of OSCEs used across the curriculum, and for those who are planning to start using OSCEs at their institution. Incorporating OSCEs into a curriculum involves a significant commitment of resources, and this teaching tip aims to assist those responsible for delivering these assessments with improving their implementation and delivery.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.046 |
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".