Supporting diverse health leadership requires active listening, observing, learning and bystanding
Bibliographic record
Abstract
Purpose Fostering diversity in health leadership is imperative as that not only enhances the quality of health care itself, but improves an organization's effectiveness and responsiveness to address the needs of a diverse population. Inequitable structures entrenched in health care such as sexism, racism and settler colonialism undermine efforts made by women from diverse backgrounds to obtain leadership roles. This paper identifies leading practices which support diverse health leadership. Design/methodology/approach A multi-methodological approach involving a targeted published and gray literature search undertaken through both traditional means and a systematic social media search, focused particularly on Twitter. A literature and social media extraction tool was developed to review and curate more than 800 resources. Items chosen included those which best highlighted the barriers faced by diverse women and those sharing tools of how allies can best support the diverse women. Findings Four core promising practices that help to disrupt the status-quo of health leadership include (1) active listening to hear and amplify voices that have been marginalized, (2) active learning to respond to translation exhaustion, (3) active observing and noticing microaggressions and their consequences and (4) active bystanding and intervention. Social implications When implemented, these practices can help to dismantle racism, sexism, ableism and otherwise challenge the status-quo in health leadership. Originality/value This paper provides an original and value-added review of the published literature and social media analysis of heretofore disparate practices of allyship, all while amplifying the voices of health leaders from marginalized communities.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".