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Record W4403731528 · doi:10.1111/jep.14167

The equivalence of a high‐stakes objective structured clinical exam adapted to suit a virtual delivery format

2024· article· en· W4403731528 on OpenAlexaff
Karen Coetzee, Tabasom Eftekari, Sandra Monteiro

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEquivalence (formal languages)Objective structured clinical examinationVirtual patientPsychologyRasch modelComputer scienceMedical educationCompetence (human resources)MedicineSocial psychologyMathematicsDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.114
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.122
GPT teacher head0.527
Teacher spread0.405 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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