Moving beyond ‘think leadership, think white male’: the contents and contexts of equity, diversity and inclusion in physician leadership programmes
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
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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