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Record W2077093593 · doi:10.5539/cis.v5n1p38

An Online Management Information System for Objective Structured Clinical Examinations

2011· article· en· W2077093593 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer and Information Science · 2011
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersHanzehogeschool GroningenUniversity of GalwayNational University of Ireland
KeywordsComputer scienceObjective structured clinical examinationQuality assuranceMedical educationMedical physicsMedicinePathology

Abstract

fetched live from OpenAlex

Objective Structured Clinical Examinations (OSCE) are adopted for high stakes assessment in medical education. Students pass through a series of timed stations demonstrating specific skills. Examiners observe and rate students using predetermined criteria. In most OSCEs low level technology is used to capture, analyse and produce results. We describe an OSCE Management Information System (OMIS) to streamline the OSCE process and improve quality assurance. OMIS captured OSCE data in real time using a Web 2.0 platform. We compared the traditional paper trail outcome with detailed real time analyses of separate stations. Using a paper trail version only one student failed the OSCE. However, OMIS identified nineteen possibly ‘incompetent’ students. Although there are limitations to the design of the study, the results are promising and likely to lead to defendable judgements on student performance.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.006
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.364
Teacher spread0.309 · 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