Equity, Diversity, and Inclusion Considerations for Leadership in Medical Education
Bibliographic record
Abstract
Abstract Canada's population is becoming increasingly diverse and the recent recognition of the need for inclusivity and diversity has led to conversations in undergraduate and graduate medical programs across the country. The intended outcomes of these conversations around representation are actions that better prepare medical graduates to meet the needs related to caring for a diverse Canadian population. It is paramount that learners see this progress toward equity, inclusivity, and diversity reflected in the leadership of their medical training programs. Actions toward this goal may be more impactful from a new understanding of leadership. This chapter focuses on a postcolonial reimagining of leadership that expands qualities that are valued, resulting in a natural diversification and increased inclusion among medical leaders. The authors write from their personal viewpoints and provide suggestions on revisioning leadership and curriculum, throughout. It is hoped that a paradigm shift in the way leaders are identified, recognized, and supported will address current challenges in medical culture and subsequent socialization of learners that influence their professional identities and ideas about who and what makes good leaders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".