Leadership Training for Department Chairs: Integrating Formal Training, Experiential Learning, and Mentorship
Bibliographic record
Abstract
The study explores the link between formal training, experiential learning, mentorship, and the leadership development of department chairs at a Canadian university. Employing a framework analysis approach grounded in transformative learning theory, data from 17 semi‐structured interviews revealed that formal training imparted essential knowledge and skills to new chairs. However, the training's influence on the chairs' development varied, depending on individual career stages and previous leadership experiences. Participants identified experiential learning as a vital element of their leadership development, with prior leadership roles providing a solid base for a successful transition. Mentorship emerged as a transformative instrument, offering timely developmental opportunities through exchanges with experienced leaders. The study concludes that strategically combining learning approaches can enhance institutional leadership capacity and facilitate faculty members' transition into department chair roles, particularly in the context of the post‐COVID‐19 pandemic and the ongoing leadership crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".