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Record W3088031178 · doi:10.1186/s12909-020-02122-4

A “fit for purpose” framework for medical education accreditation system design

2020· review· en· W3088031178 on OpenAlexaff
Sarah Taber, Nesibe Akdemir, Lisa Gorman, Marta van Zanten, Jason R. Frank

Bibliographic record

VenueBMC Medical Education · 2020
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsAccreditationOperationalizationCertification and AccreditationMedical educationScope (computer science)Health careBest practiceComputer scienceProcess managementMedicineEngineering managementPolitical scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Accreditation is a key feature of many medical education systems, helping to ensure that programs teach and assess learners according to applicable standards, provide optimal learning environments, and produce professionals who are competent to practise in challenging and evolving health care systems. Although most medical education accreditation systems apply similar standards domains and process elements, there can be substantial variation among accreditation systems at the level of design and implementation. A discussion group at the 2013 World Summit on Outcomes-Based Accreditation examined best practices in health professional education accreditation systems and identified that the literature examining the effectiveness of different approaches to accreditation is scant. Although some frameworks for accreditation design do exist, they are often specific to one phase of the medical education continuum. MAIN TEXT: This paper attempts to define a framework for the operational design of medical education accreditation that articulates design options as well as their contextual and practical implications. It assumes there is no single set of best practices in accreditation system development but, rather, an underlying set of design decisions. A "fit for purpose" approach aims to ensure that a system, policy, or program is designed and operationalized in a manner best suited to local needs and contexts. This approach is aligned with emerging models for education and international development that espouse decentralization. CONCLUSION: The framework highlights that, rather than a single best practice, variation among accreditation systems is appropriate provided that is it tailored to the needs of local contexts. Our framework is intended to provide guidance to administrators, policy-makers, and educators regarding different approaches to medical education accreditation and their applicability and appropriateness in local contexts.

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)0.101
metaresearch head score (Gemma)0.080
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.101
Threshold uncertainty score0.534

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1010.080
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0080.006
Science and technology studies0.0060.029
Scholarly communication0.0170.012
Open science0.0070.009
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0050.002

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 head0.114
GPT teacher head0.475
Teacher spread0.362 · 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 designTheoretical or conceptual
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

Citations39
Published2020
Admission routes1
Has abstractyes

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