Moving beyond ‘think leadership, think white male’: the contents and contexts of equity, diversity and inclusion in physician leadership programmes
Bibliographic record
Abstract
The lack of both women and physicians from groups under-represented in medicine (UIM) in leadership has become a growing concern in healthcare. Despite increasing recognition that diversity in physician leadership can lead to reduced health disparities, improved population health and increased innovation and creativity in organisations, progress toward this goal is slow. One strategy for increasing the number of women and UIM physician leaders has been to create professional development opportunities that include leadership training on equity, diversity and inclusivity (EDI). However, the extent to which these concepts are explored in physician leadership programming is not known. It is also not clear whether this EDI content challenges structural barriers that perpetuate the status quo of white male leadership. To explore these issues, we conducted an environmental scan by adapting Arksey and O’Malley’s scoping review methodology to centre on three questions: How is EDI currently presented in physician leadership programming? How have these programmes been evaluated in the peer-reviewed literature? How is EDI presented and discussed by the wider medical community? We scanned institutional websites for physician leadership programmes, analysed peer-reviewed literature and examined material from medical education conferences. Our findings indicate that despite an apparent increase in the discussion of EDI concepts in the medical community, current physician leadership programming is built on theories that fail to move beyond race and gender as explanatory factors for a lack of diversity in physician leadership. To address inequity, physician leadership curricula should aim to equip physicians to identify and address the structural factors that perpetuate disparities.
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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 | 0.066 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 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".