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Record W2788767687 · doi:10.2196/jmir.7548

Impact of a Student-Driven, Virtual Patient Application on Objective Structured Clinical Examination Performance: Observational Study

2018· article· en· W2788767687 on OpenAlexaffabout
David Bergeron, Jean‐Nicolas Champagne, Wen Qi, Maxime Dion, Julie F. Thériault, Jean‐Sébastien Renaud

Bibliographic record

VenueJournal of Medical Internet Research · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSummative assessmentObjective structured clinical examinationMedical educationObservational studyEducational measurementVirtual patientFormative assessmentPsychologyClinical clerkshipPeer feedbackComputer scienceMedicineMathematics educationCurriculumPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Peer-assisted learning (PAL) refers to a learning activity whereby students of similar academic level teach and learn from one another. Groupe de perfectionnement des habiletés cliniques (Clinical Skills Improvement Group), a student organization at Université Laval, Canada, propelled PAL into the digital era by creating a collaborative virtual patient platform. Medical interviews can be completed in pairs (a student-patient and a student-doctor) through an interactive Web-based application, which generates a score (weighted for key questions) and automated feedback. OBJECTIVES: The aim of the study was to measure the pedagogical impact of the application on the score at medical interview stations at the summative preclerkship Objective Structured Clinical Examination (OSCE). METHODS: We measured the use of the application (cases completed, mean score) in the 2 months preceding the OSCE. We also accessed the results of medical interview stations at the preclerkship summative OSCE. We analyzed whether using the application was associated with higher scores and/or better passing grades (≥60%) at the OSCE. Finally, we produced an online form where students could comment on their appreciation of the application. RESULTS: Of the 206 students completing the preclerkship summative OSCE, 170 (82.5%) were registered users on the application, completing a total of 3133 cases (18 by active user in average, 7 minutes by case in average). The appreciation questionnaire was answered online by 45 students who mentioned appreciating the intuitive, easy-to-use, and interactive design, the diversity of cases, and the automated feedback. Using the application was associated with reduced reported stress, improved scores (P=.04), and improved passing rates (P=.11) at the preclerkship summative OSCE. CONCLUSIONS: This study suggests that PAL can go far beyond small-group teaching, showing students' potential to create helpful pedagogical tools for their peers.

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)0.003
metaresearch head score (Gemma)0.018
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.188
GPT teacher head0.569
Teacher spread0.381 · 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".

Quick stats

Citations6
Published2018
Admission routes2
Has abstractyes

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