Rethinking how we educate our future physicians: the Queen’s-Lakeridge Health Medical Degree Family Medicine program: an innovative approach to medical education
Bibliographic record
Abstract
Canadian healthcare, particularly family medicine, is experiencing a capacity crisis. To address this challenge, systems-level reform is required. Specific to medical education, there is an urgency for educational institutions to prepare the next generation of physicians to meet Canadians' healthcare needs. This will require not only the expansion of the number of physicians trained, but consideration of the specialty mix and types of practice those physicians are prepared to undertake. Building on an existing collaborative partnership, the Queen's-Lakeridge Health Doctor of Medicine - Family Medicine Program (QLH MD FM) launched in 2023. This initiative is designed to address the needs of Canadians through purposeful recruitment and the development of learners interested in, and committed to, careers in family medicine. This paper shares the experiences of the QLH MD FM team, and the steps taken in conceptualizing and implementing this educational initiative. A program description is provided, focusing on the six program pillars: (a) admissions, (b) curriculum, (c) faculty and staff engagement, (d) community engagement, (e) infrastructure and supports, and (f) learner experience. The QLH MD FM Program is an innovative approach to medical education that emphasizes an authentic focus on addressing the complex healthcare needs of individuals and their communities.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".