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Record W4323321760 · doi:10.37964/cr24766

Virtual objective structured clinical examinations — a novel approach to teaching and evaluating leadership skills in medical students

2023· article· en· W4323321760 on OpenAlexvenueno aff
Michael Aw, Ahmed Shoeib, Craig Campbell, Charles A. Su

Bibliographic record

VenueCanadian Journal of Physician Leadership · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationObjective structured clinical examinationCurriculumObjectivity (philosophy)Communication skillsPsychologyMedicinePedagogy

Abstract

fetched live from OpenAlex

The objective structured clinical examination (OSCE) is a simulation-based method of learning and assessment that allows for mistakes and feedback. Its uniformity, objectivity, and reproducibility are among its greatest strengths. Unfortunately, OSCEs rarely directly assess non-clinical skills or focus on leadership skills, such as conflict management. Leadership training is an underrepresented component of the medical school curriculum. Although OSCEs cannot evaluate leadership in its entirety, we have demonstrated the feasibility of using OSCEs to simulate realistic scenarios for students to apply and practise communication, collaboration and professional skills associated with leadership.

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 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)0.007
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.169
GPT teacher head0.431
Teacher spread0.263 · 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 teacher head, not a consensus.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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