Developing Leadership within an Academic Medical Department in Canada: A Road Map for Increasing Leadership Span
Bibliographic record
Abstract
Medicine is dependent on strong leaders to advance innovation in the clinical care of patients. In most academic medical streams, there is no explicit system-wide approach for succession planning and leadership development. In late 2009, it was clear to the authors' department that they were at risk of losing high-potential individuals and division heads. Succession Planning and Needs Assessment (SPAN) was introduced to the department executive in late 2009 and endorsed in mid-2010. An executive coach was hired to assist in identifying emerging leaders and the skills needing to be developed within a mentorship cycle for leaders to be successful. A group of emerging leaders plus observer senior leaders worked between June and October 2010 to develop a manual that would provide guidance to the department executive. Since June 2010 a succession plan has been in place, allowing allocation of leadership roles. A group of 18 individuals has met four times to establish the elements of leadership development. A manual has been endorsed that includes elements such as the traits needed to be considered an emerging leader; the skills agreed on as important to develop; and the mentorship cycle needed. The group has also proposed a coordinator role and a budget for resource material. Departmental leadership development initiatives are important for succession planning and engagement of high-potential academics, who eventually will become our future leaders. In this article, the authors propose a cohort approach to piloting department initiatives that make a difference to developing 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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".