Virtual Objective Structured Clinical Examination Experiences and Performance in Physical Medicine and Rehabilitation Residency
Bibliographic record
Abstract
BACKGROUND: Virtual education has been described before and during the COVID-19 pandemic. Studies evaluating virtual objective structured clinical examinations with postgraduate learners are lacking. This study (1) evaluated the experiences of all participants in a virtual objective structured clinical examination and (2) assessed the validity and reliability of selected virtual objective structured clinical examination stations for skills in physical medicine and rehabilitation. METHODS: Convergent mixed-methods design was used. Participants included three physical medicine and rehabilitation residency programs holding a joint virtual objective structured clinical examination. Analysis included descriptive statistics and thematic analysis. Performance of virtual to previous in-person objective structured clinical examination was compared using independent t tests. RESULTS: Survey response rate was 85%. No participants had previous experience with virtual objective structured clinical examination. Participants found the virtual objective structured clinical examination to be acceptable (79.4%), believable (84.4%), and valuable for learning (93.9%). No significant differences between in-person and virtual objective structured clinical examination scores was found for three-fourth stations and improved scores in one fourth. Four themes were identified: (1) virtual objective structured clinical examinations are better for communication stations; (2) significant organization is required to run a virtual objective structured clinical examination; (3) adaptations are required compared with in-person objective structured clinical examinations; and (4) virtual objective structured clinical examinations provide improved accessibility and useful practice for virtual clinical encounters. CONCLUSIONS: Utility of virtual objective structured clinical examinations as a component of a program of assessment should be carefully considered and may provide valuable learning opportunities going forward.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".