Emerging concepts in the CanMEDS physician competency framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The CanMEDS physician competency framework will be updated in 2025. The revision occurs during a time of disruption and transformation to society, healthcare, and medical education caused by the COVID-19 pandemic and growing acknowledgement of the impacts of colonialism, systemic discrimination, climate change, and emerging technologies on healthcare and training. To inform this revision, we sought to identify emerging concepts in the literature related to physician competencies. Methods: Emerging concepts were defined as ideas discussed in the literature related to the roles and competencies of physicians that are absent or underrepresented in the 2015 CanMEDS framework. We conducted a literature scan, title and abstract review, and thematic analysis to identify emerging concepts. Metadata for all articles published in five medical education journals between October 1, 2018 and October 1, 2021 were extracted. Fifteen authors performed a title and abstract review to identify and label underrepresented concepts. Two authors thematically analyzed the results to identify emerging concepts. A member check was conducted. Results: 1017 of 4973 (20.5%) of the included articles discussed an emerging concept. The thematic analysis identified ten themes: Equity, Diversity, Inclusion, and Social Justice; Anti-racism; Physician Humanism; Data-Informed Medicine; Complex Adaptive Systems; Clinical Learning Environment; Virtual Care; Clinical Reasoning; Adaptive Expertise; and Planetary Health. All themes were endorsed by the authorship team as emerging concepts. Conclusion: This literature scan identified ten emerging concepts to inform the 2025 revision of the CanMEDS physician competency framework. Open publication of this work will promote greater transparency in the revision process and support an ongoing dialogue on physician competence. Writing groups have been recruited to elaborate on each of the emerging concepts and how they could be further incorporated into CanMEDS 2025.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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 it