How to Lead the Way Through Complexity, Constraint, and Uncertainty in Academic Health Science Centers
Bibliographic record
Abstract
Academic medicine is in an era of unprecedented and constant change due to fluctuating economies, globalization, emerging technologies, research, and professional and educational mandates. Consequently, academic health science centers (AHSCs) are facing new levels of complexity, constraint, and uncertainty. Currently, AHSC leaders work with competing academic and health service demands and are required to work with and are accountable to a diversity of stakeholders. Given the new challenges and emerging needs, the authors believe the leadership methods and approaches AHSCs have used in the past that led to successes will be insufficient. In this Article, the authors propose that AHSCs will require a unique combination of old and new leadership approaches specifically oriented to the unique complexity of the AHSC context. They initially describe the designer (or hierarchical) and heroic (military and transformational) approaches to leadership and how they have been applied in AHSCs. While these well-researched and traditional approaches have their strengths in certain contexts, the leadership field has recognized that they can also limit leaders' abilities to enable their organizations to be engaged, adaptable, and responsive. Consequently, some new approaches have emerged that are taking hold in academic work and professional practice. The authors highlight and explore some of these new approaches-the authentic, self, shared, and network approaches to leadership-with attention to their application in and utility for the AHSC context.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".