Where is the lifestyle medicine in the Canadian undergraduate medical education curricula? A content analysis
Bibliographic record
Abstract
Background: Lifestyle Medicine (LM) focuses on preventing and managing non-communicable diseases (NCDs) through evidence-based behavioural and therapeutic interventions. Despite the established efficacy of LM, its integration into Canadian undergraduate medical education (UGME) remains largely unexamined. Methods: We employed a deductive and inductive summative content analysis methodology. We collected publicly available course and program descriptions for each UGME program across Canada. Further, we contacted deans/curriculum leads for additional curriculum documentation. Documents were coded according to references to various domains of LM as defined by the American College of LM and British Society of LM. Results: The sample comprised 13 UGME programs, with 1327 documents included for the final review. Notable variability across institutions was noted with reference to LM integration. LM topics were more frequently included in pre-clerkship and required courses compared to clerkship and electives. Notably, nutrition, mental wellbeing, and physical activity were most frequently referenced, while sleep health and social connectedness were less represented. Conclusions: To our knowledge, this study is the first to formally map the current practices of LM integration in Canadian UGME. Although there were some study limitations (e.g., exclusion of 25% of Canadian UGME programs), this mapping is integral to identify the current state of the curricula and inform future educational initiatives to enhance medical trainees' LM-related knowledge and skills. This in turn, may potentially help address modifiable risk factors for NCDs and improve population health outcomes.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.036 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".