The equivalence of a high‐stakes objective structured clinical exam adapted to suit a virtual delivery format
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic necessitated rapid adaptation of clinical competence assessments, including the transition of Objective Structured Clinical Examinations (OSCE) from in-person to virtual formats. This study investigates the construct equivalence of a high-stakes OSCE, originally designed for in-person delivery, when adapted for a virtual format. METHODS: A retrospective analysis was conducted using OSCE scores from the Internationally Educated Nurse Competency Assessment Program (IENCAP®). Data were collected from 15 exam administrations between January 2018 and June 2022, encompassing 2021 examinees (1936 in-person, 85 virtual). The Many-Facet Rasch Measurement (MFRM) model was employed to analyze the invariance of examinee ability, case difficulty, and criteria difficulty across in-person and virtual formats. RESULTS: Results revealed overall examinee ability estimates remained invariant regardless of the OSCE format, while invariant violations were identified in only three of the 15 cases (N = 20%) adapted to suit the virtual format. The most significant adaptation, namely the use of a verbal physical examination to suit the virtual context achieved equivalence to its hands-on in-person counterpart given evidence of invariance across criteria estimates. Interestingly, criteria scores in invariant violated cases displayed a higher level of stability or consistency across the virtual OSCE formats versus their in-person counterpart highlighting a potential benefit of the virtual versus in-person format and potentially linked to the verbal physical examination. CONCLUSION: The study found that while examinee ability and case difficulty estimates exhibited some invariance between in-person and virtual OSCE formats, criteria involving physical assessments faced challenges in maintaining construct equivalence. These findings highlight the need for careful consideration in adapting high-stakes clinical assessments to virtual formats to ensure fairness and reliability.
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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 | 0.016 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".