Leadership Succession at Regional Medical Campuses: What incoming leaders might want to know from their predecessors
Bibliographic record
Abstract
Succession planning and changeover between outgoing and incoming leaders within medical education is an important but largely neglected topic (Rayburn, Grigsby & Brubaker, 2016). The paucity of literature is even more apparent regarding leadership transitioning at regional medical campuses (RMC). With this paper, perhaps one of the first to inform this topic, we hope to bridge this gap by assembling the shared perspectives of an administrator, senior faculty member and a learner from the same RMC. Specifically, this work will focus on the following two questions: Based on our collective experiences what are the critical issues facing an incoming RMC dean? Are there practical strategies which might assist an incoming RMC dean with the leadership transition process? This commentary is the result of our collective experience at Western University’s Windsor Campus, a 10-year old regional medical campus of the Schulich School of Medicine & Dentistry located in southwestern Ontario, Canada. We make the assumption that RMC’s encountering leadership transitions are adequately funded, in this way incoming deans can properly attend to effective leadership succession processes. This paper might be of particular interest to those who are personally transitioning as new RMC deans or who will soon take on such a leadership role. We have no conflicts of interest to declare.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".