A tale of two frameworks: charting a path to lifelong learning for physician leaders through CanMEDS and LEADS
Bibliographic record
Abstract
PURPOSE: The purpose of this paper was to determine the complementarity between the Canadian Medical Education Directions for Specialists (CanMEDS) physician competency and LEADS leadership capability frameworks from three perspectives: epistemological, philosophical and pragmatic. Based on those findings, the authors propose how the frameworks collectively layout pathways of lifelong learning for physician leadership. DESIGN/METHODOLOGY/APPROACH: Using a qualitative approach combining critical discourse analysis with a modified Delphi, the authors examined "How complementary the CanMEDS and LEADS frameworks are in guiding physician leadership development and practice" with the following sub-questions: What are the similarities and differences between CanMEDS and LEADS from: An epistemological and philosophical perspective? The perspective of guiding physician leadership training and practice? How can CanMEDS and LEADS guide physician leadership development from medical school to retirement? FINDINGS: Similarities and differences exist between the two frameworks from philosophical and epistemological perspectives with significant complementarity. Both frameworks are founded on a caring ethos and value physician leadership - CanMEDS (for physicians) and LEADS (physicians as one of many professions) define leadership similarly. The frameworks share beliefs in the function of leadership, embrace a belief in distributed leadership, and although having some philosophical differences, have a shared purpose (preparing for changing health systems). Practically, the frameworks are mutually supportive, addressing leadership action in different contexts and where there is overlap, complement one another in intent and purpose. ORIGINALITY/VALUE: To the best of the authors' knowledge, this is the first paper to map the CanMEDS (physician competency) and LEADS (leadership capabilities) frameworks. By determining the complementarity between the two, synergies can be used to influence physician leadership capacity needed for today and the future.
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.
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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.044 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".