Core Competencies for Students Entering Medical School: Reaching Pan-Canadian Consensus for Inclusive and Accessible Medical Education
Bibliographic record
Abstract
ABSTRACT: A socially accountable physician workforce must include disabled learners and providers. However, current Canadian Technical Standards (TS) for medical school admissions create barriers to their inclusion. These standards overlook advances in assistive technology, universal design, evolving inclusion practices, and legal protections. Replacing the TS required consensus, but traditional methods of achieving consensus on disability inclusion risk reinforcing ableism in medical education. To address challenges with existing TS, the Association of Faculties of Medicine of Canada (AFMC) formed the "Re-envisioning TS Working Group," using a novel consensus approach grounded in disability inclusion and critical disability discourse. Guided by transparency, accessibility, and respect for disability as diversity, the group prioritized engagement with disabled physicians, educators, scholars, and learners. The WG followed 5 stages: (1) identifying key concepts and reviewing literature on TS reform and ableism; (2) examining relevant legislation and case law; (3) drafting functional Core Competencies; (4) consulting partners across the medical education continuum; and (5) presenting outcomes to the AFMC Board, highlighting a commitment to disability inclusion in undergraduate medical education. The AFMC Board unanimously endorsed the "Report on Re-Envisioning Technical Standards," including the "Desired Outcomes" and the "Core Competencies for Entering Medical Students." The AFMC's adoption of functional Core Competencies is a significant step toward inclusion and support for learners with disabilities in Canadian medical education. Medical schools should adopt these competencies, combat ableism, and invest in universal design to promote access. Accommodation support should extend from admission through postgraduate training to independent practice. Finally, efforts to foster an inclusive culture and contribute to a healthy, diverse physician workforce must be evaluated as part of medical schools' social accountability mandate.
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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 | 0.081 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".