Redefining professionalism to improve health equity in competency based medical education (CBME): A qualitative study
Bibliographic record
Abstract
Purpose There is a pressing need to address all forms of anti-oppression in medicine, given systemic harm and inequities in care and outcomes for patients and health care professionals from equity-deserving groups. Revising definitions of professionalism used in competency-based education can incorporate new professional competencies for physicians to identify and eliminate the root causes of these inequities. This study redefined the CanMEDS Professionalism definition to centre perspectives of equity-deserving groups. Methods In this qualitative study there were two phases. The authors conducted individual semi-structured interviews with participants representing equity-deserving population groups to understand their perspectives on and iteratively build a definition of medical professionalism. Then, the authors undertook a consensus-building process, a modified nominal group technique, using focus groups with community members from equity-deserving groups and healthcare providers to verify findings and arrive at an updated definition of medical professionalism. Results Four main themes were identified: 1) healthcare at the margins; 2) equity-oriented domains of professionalism; 3) structural professionalism; and 4) supporting improved professionalism. These themes were incorporated into a consensus-based definition of medical professionalism, with a focus on anti-oppression, anti-racism, accountability, safety, and equity. Conclusions The authors propose a new definition of medical professionalism that embeds anti-oppression, including anti-racism, as critical competencies in clinical practice and education.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".