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Record W4387806594 · doi:10.3390/educsci13101057

Assessments Used for Summative Purposes during Internal Medicine Specialist Training: A Rapid Review

2023· review· en· W4387806594 on OpenAlexaboutno aff
Scott D. Patterson, Louise Shaw, Michelle M. Rank, Brett Vaughan

Bibliographic record

VenueEducation Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsSummative assessmentMedicineMedical educationFormative assessmentClinical PracticeFamily medicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

Assessments used for summative purposes of patient-facing clinical competency in specialist internal medicine training are high-stakes, both to doctors in training, as it is a prerequisite for qualification, as well as their community of prospective patients. A rapid review of the literature evaluated methods of assessments used for summative purposes of patient-facing clinical competency during specialist internal medicine training in Australia. Four online databases identified literature published since the year 2000 that reported on summative assessment in specialist medical training. Two reviewers screened and selected eligible studies and extracted data, with a focus on evidence of support for the criteria for good assessment as set out in the 2010 Ottawa Consensus framework for good assessment. Ten eligible studies were included. Four studied the mini-clinical evaluation exercise (mini-CEX), two the Royal Australasian College of Physicians short case exam, three a variety of Entrustable Professional Activities (EPAs) or summative entrustment and progression review processes, and one a novel clinical observation tool. The mini-CEX assessment demonstrated the most evidence in support of the Ottawa criteria. There was a paucity of published evidence regarding the best form of summative assessment of patient-facing clinical competency in specialist internal medicine 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 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)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.406
GPT teacher head0.583
Teacher spread0.177 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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