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Record W2169632315 · doi:10.3109/0142159x.2015.1031735

Continuous quality improvement in an accreditation system for undergraduate medical education: Benefits and challenges

2015· article· en· W2169632315 on OpenAlexaffabout
Barbara Barzansky, Dan Hunt, Geneviève Moineau, Duck Sun Ahn, Chi‐Wan Lai, Holly J. Humphrey, Linda Peterson

Bibliographic record

VenueMedical Teacher · 2015
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsCanadian Medical AssociationAssociation of Universities and Colleges of Canada
Fundersnot available
KeywordsAccreditationMedical educationQuality (philosophy)Quality managementMedicinePsychologyEngineering ethicsEngineeringOperations managementManagement system

Abstract

fetched live from OpenAlex

BACKGROUND: Accreditation reviews of medical schools typically occur at fixed intervals and result in a summative judgment about compliance with predefined process and outcome standards. However, reviews that only occur periodically may not be optimal for ensuring prompt identification of and remediation of problem areas. AIMS: To identify the factors that affect the ability to implement a continuous quality improvement (CQI) process for the interval review of accreditation standards. METHODS: Case examples from the United States, Canada, the Republic of Korea and Taiwan, were collected and analyzed to determine the strengths and challenges of the CQI processes implemented by a national association of medical schools and several medical school accrediting bodies. The CQI process at a single medical school also was reviewed. RESULTS: A functional CQI process should be focused directly on accreditation standards so as to result in the improvement of educational quality and outcomes, be feasible to implement, avoid duplication of effort and have both commitment and resource support from the sponsoring entity and the individual medical schools. CONCLUSIONS: CQI can enhance educational program quality and outcomes, if the process is designed to collect relevant information and the results are used for program improvement.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.102
GPT teacher head0.401
Teacher spread0.299 · 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 designOther design
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

Citations83
Published2015
Admission routes2
Has abstractyes

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