Bridging the continuum: Analysis of the alignment of undergraduate and postgraduate accreditation standards
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".