MétaCan
Menu
Back to cohort
Record W2109159534 · doi:10.3109/0142159x.2014.910298

Bridging the continuum: Analysis of the alignment of undergraduate and postgraduate accreditation standards

2014· article· en· W2109159534 on OpenAlexaffabout
Susan E. Andrew, Anna Oswald, Kent Stobart

Bibliographic record

VenueMedical Teacher · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Alberta
FundersLaboratório Central de Microscopia Eletrônica, Universidade Federal de Santa CatarinaChina Academy of Chinese Medical Sciences
KeywordsAccreditationBridging (networking)Medical educationMedicinePsychologyComputer science

Abstract

fetched live from OpenAlex

Several influential national/international bodies including The Bologna Accord, The Carnegie Foundation and The Future of Medical Education in Canada (FMEC) have called for increased coordination across the medical education continuum. FMEC recognizes accreditation as a "powerful lever" and encourages the alignment of undergraduate and postgraduate standards. The Carnegie Foundation includes a similar call for the creation of a more coherent accreditation system. As a first step, using the Canadian context, we present a methodological approach that assesses the example of how well LCME/CACMS undergraduate accreditation standards align with the Royal College of Physician and Surgeons of Canada (RCPSC) postgraduate training standards. We analyzed how closely the 132 LCME/CACMS Medical School accreditation standards aligned with the 155 post-graduate standards from the RCPSC accreditation General Standards (A and B). This comparative evaluation demonstrates that the standards do not align closely. Gaps, redundancies and key differences are highlighted. These results are the first step in understanding how accreditation needs to be adapted and re-aligned across the education continuum to provide consistent and coordinated training and these methods could easily be applied to other contexts and jurisdictions.

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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.340
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.318
Teacher spread0.307 · 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.

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

Citations12
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueMedical TeacherSame topicInnovations in Medical EducationFrench-language works237,207