Transforming the landscape of obesity education - The Canadian obesity education competencies
Bibliographic record
Abstract
Background: With ongoing gaps in obesity education delivery for health professions in Canada and around the world, a transformative shift is needed to address and mitigate weight bias and stigma, and foster evidence-based approaches to obesity assessment and care in the clinical setting. Obesity Canada has created evidence-based obesity competencies for medical education that can guide curriculum development, assessment and evaluation and be applied to health professionals' education programs in Canada and across the world. Methods: The Obesity Canada Education Action Team has seventeen members in health professions education and research along with students and patient experts. Through an iterative group consensus process using four guiding principles, key and enabling obesity competencies were created using the 2015 CanMEDS competency framework as its foundation. These principles included the representation of all CanMEDS Roles throughout the competencies, minimizing duplication with the original CanMEDS competencies, ensuring obesity focused content was informed by the 2020 Adult Obesity Clinical Practice Guidelines and the 2019 US Obesity Medication Education Collaborative Competencies, and emphasizing patient-focused language throughout. Results: A total of thirteen key competencies and thirty-seven enabling competencies make up the Canadian Obesity Education Competencies (COECs). Conclusion: The COECs embed evidence-based approaches to obesity care into one of the most widely used competency-based frameworks in the world, CanMEDS. Crucially, these competencies outline how to address and mitigate the damaging effects of weight bias and stigma in educational and clinical settings. Next steps include the creation of milestones and nested Entrustable Professional Activities, a national report card on obesity education for undergraduate medical education in Canada, and Free Open Access Medication Education content, including podcasts and infographics, for easier adoption into curriculum around the world and across the health professions spectrum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".