Creating an Interprofessional Network in Lifestyle Medicine: The Journey of the Canadian Academy of Lifestyle Medicine
Bibliographic record
Abstract
Canada's population is increasing, and aging. These demographic patterns are accompanied by a growing awareness and evidence base of the benefits to society of leading a healthy and active life. The Canadian Academy of Lifestyle Medicine (CALM) was created to fill a knowledge gap in the Canadian public: how to lead a healthier and more active life. CALM aimed to address these challenges by confronting the lack of assistance modern medicine provides. As a diverse collaborative network using a lifestyle medicine philosophy, CALM's objective was to generate discussions and examine lifestyle medicine approaches to improving overall health and well-being for Canadians. CALM aimed to engage patients whose access to health care is through a physician and provide an innovative platform to support care and healthy decision making. Despite perceived widespread support, intense planning, and extensive development, CALM was slow to gain traction and realize its full potential. This article describes the experiences and lessons learned in creating CALM from the perspective of the leadership team. Although most CALM activities have ceased, virtual space and social media remain active so too does the work of the leadership team, striving to enable Canadians to develop behaviors that will improve their lifestyle, and their overall well-being.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.070 | 0.016 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".