Six Principles for Developing Leadership Training Ecosystems in Health Care
Bibliographic record
Abstract
Leadership education in medicine is evolving to better meet the challenges of health care complexity, interprofessional practice, and threats from viruses and budget cuts alike. In this commentary, the authors build upon the findings of a scoping review by Matsas and colleagues, published in the same issue, and ask us to imagine what a learning ecosystem around leadership might look like. They subsequently engage in their own synthesis of leadership development literature and propose 6 key principles for medical educators and health care leaders to consider when designing leadership development within their educational ecosystems: (1) apply a conceptual framework; (2) scaffold development-oriented approaches; (3) accommodate individual levels of adult development; (4) integrate diversity of perspective; (5) interweave theory, practice, and reflection; and (6) recognize the broad range of leadership conceptualization.
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.
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 | 0.086 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".