Implementation of the Lifestyle Medicine Residency Curriculum in a Francophone Family Medicine Residency at Université Laval: Lessons Learned From Québec, Canada
Bibliographic record
Abstract
BACKGROUND: Lifestyle medicine (LM) is gaining recognition in medical education. Université Laval, a Francophone university, is the first university in Canada to implement the Lifestyle Medicine Residency Curriculum (LMRC) developed by the American College of Lifestyle Medicine. This study aimed to evaluate the feasibility of implementing LM training for family residents in Quebec City. METHOD: Eight mentors adapted the English LMRC to Quebec's healthcare and cultural context while maintaining its core content. In September 2022, 16 family medicine residents participated in the program. Mentors collaborated with program directors to develop French-language materials, contextualize content, and create an online LM platform. Feedback from residents and mentors was collected through surveys, focus groups, and informal discussions to guide continuous improvements. RESULTS: Fifteen of 16 enrolled residents completed the program. Average attendance at monthly sessions was 70 %, with absences mainly due to night shifts or regional rotation. Resident showed strong engagement, with 100% completing modules asynchronously. Key factors for successful implementation included faculty mentors' and residents' engagement, and French-language materials tailored to the Quebec healthcare system. Challenges included limited French LM resources and a lack of co-located interdisciplinary teams. Strategic solutions involved creating a centralized online platform, protected learning time, aligning the program with existing curricula, and partnerships with community programs. CONCLUSION: Implementing LMRC demonstrated the feasibility of integrating LM training into a Francophone family medicine residency. Lessons learned may inform broader adoption in diverse linguistic and cultural settings.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".