A Mixed-Methods, Validity Informed Evaluation of a Virtual OSCE for Undergraduate Medicine Clerkship
Bibliographic record
Abstract
Background: Pandemic-related learning environment disruptions have threatened clinical skills development and assessment for medical students and prompted a shift to virtual objective structured clinical examinations (vOSCEs). This study explores the benefits/limitations of vOSCEs from the perspective of key stakeholders and makes recommendations for improving future vOSCEs. Materials and Methods: Using a mixed-methods, utilization-focused program evaluation, we looked at feasibility and implementation evidence that addresses content, response process, and feasibility as per Messick’s validity framework. The analysis of test data was reviewed to inform reliability, acceptability, and consequential validity. A 14-question online survey was sent to both students and faculty followed by stakeholder focus groups. Descriptive statistics were collected, and deidentified transcripts independently reviewed and analyzed via constant, comparative, and descriptive thematic analysis. Results: The survey results showed the vOSCE was a feasible option for assessing history-taking, clinical reasoning, and counseling skills. Limitations were related to assessing subtle aspects of communications skills, physical examination competencies, and technical disruptions. Beyond benefits and drawbacks, major qualitative themes included recommendations for faculty development, technology limitations, professionalism, and equity in the virtual environment. The reliability of the six vOSCE stations reached a satisfactory level with a G-coefficient of 0.51/0.53. Conclusions: The implementation of a virtual, summative clerkship OSCE demonstrated adequate validity evidence and feasibility. The key lessons learned relate to faculty development content and ensuring equity and academic integrity. Future study directions include examining the role of vOSCEs in the assessment of virtual care competencies and the larger role of OSCEs in the context of workplace-based assessment and competency-based medical education.
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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 | 0.122 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".