A Crucial Moment for Reflection on the Importance of Ethical Leadership in Academic Medicine
Bibliographic record
Abstract
The extreme disturbances caused by the COVID -19 pandemic on our academic medical centers compounded by a recurrent surge of violence against people of color have reopened our wounds exposing fragility, inequality, and continued racial disparities in society and health. At the center of this severe institutional disruption, leaders will be compelled to take action to keep their constituents and patients safe and their hospitals and departments afloat during and after a pandemic, all while simultaneously addressing and implementing the cultural changes required to eliminate systemic racism and discrimination. Organizational disruptions of this magnitude will naturally test one's principles, loyalties and responsibilities while challenging the practical burdens of leadership. If the goal of responding to these upheavals is to bring them to resolution and ultimately to bring about organizational change for the better, ethical leadership is critical. Applying ethical principles allows leaders to chart clear paths to solutions both in the short and long term. We review the principles of ethical leadership exemplified by a case illustration and provide a novel resource to help ensure ethical leadership in academic medicine and beyond.
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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 | 0.046 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.051 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.026 | 0.073 |
| Insufficient payload (model declined to judge) | 0.004 | 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".