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Record W2107946647 · doi:10.1080/01421590701477464

Evaluation of the usefulness of simulated clinical examination in family-medicine residency program

2007· article· en· W2107946647 on OpenAlexaff
Vernon Curran, Roger Butler, Pauline Duke, William Eaton, Scott Moffatt, Greg Sherman, Madge Pottle

Bibliographic record

VenueMedical Teacher · 2007
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedical educationFamily medicineMedicineObjective structured clinical examinationConstructiveExploratory researchPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: This study reports on an evaluation of the usefulness of the Simulated Clinical Examination (SCE) method as a means of assessing the clinical-skill competencies of entering Postgraduate year 1 (PGY1) family-medicine residents. METHODS: PGY1 family-medicine residents participated in a SCE encompassing clinical encounters with standardized patients. Residents were asked to complete pre-evaluation and post-evaluation surveys, and faculty and residents participated in separate focus groups. RESULTS: The SCE was perceived as a useful method during the early phases of postgraduate training for assessing clinical-skill competencies, providing constructive feedback to residents, enhancing self-awareness, and enhancing confidence. CONCLUSIONS: This exploratory study suggests that the SCE, as an assessment method, can have beneficial effects on learning and the fostering of clinical-skill competencies during postgraduate training.

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)0.008
metaresearch head score (Gemma)0.036
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 score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.203
GPT teacher head0.502
Teacher spread0.300 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

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