"Mind the Gap": Seven Key Issues in Aligning Medical Education and Healthcare Policy
Bibliographic record
Abstract
To ensure an adequate supply of physicians for the future, Canadian faculties of medicine have been expanding and modifying physician training at the undergraduate and postgraduate levels with the intention of producing more physicians and addressing long-standing challenges in the Canadian physician workforce. While these medical education initiatives may partly address these goals, the lack of alignment between health services policy and education policy may well lead to failures and disappointing results. The authors argue that changes in related healthcare policy are required both to support the intended outcomes and to sustain innovations in medical education. From their perspective as medical educators, the authors describe seven key gaps in this alignment, identify those who are in a position to address them and call for ongoing opportunities to identify, discuss and address alignment of policy with other initiatives at the national and provincial levels.
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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 | 0.059 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.029 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.025 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".