Understanding the Needs of Department Chairs in Academic Medicine
Bibliographic record
Abstract
PURPOSE: The challenges for senior academic leadership in medicine are significant and becoming increasingly complex. Adapting to the rapidly changing environment of health care and medical education requires strong leadership and management skills. This article provides empirical evidence about the intricate needs of department chairs to provide insight into the design of support and development opportunities. METHOD: In an exploratory case study, 21 of 25 (84%) department chairs within a faculty of medicine at a large Canadian university participated in semistructured interviews from December 2009 to February 2010. The authors conducted an inductive thematic analysis and identified a coding structure through an iterative process of relating and grouping of emerging themes. RESULTS: These participants were initially often insufficiently prepared for the demands of their roles. They identified a specific set of needs. They required cultural and structural awareness to navigate their hospital and university landscapes. A comprehensive network of support was necessary for eliciting advice and exchanging information, strategy, and emotional support. They identified a critical need for infrastructure growth and development. Finally, they stressed that they needed improvement in both effective interpersonal and influence skills in order to meet their mandate. CONCLUSIONS: Given the complexities and emotional burden of their role, it is necessary for chairs to have a range of supports and capabilities to succeed in their roles. Their leadership effectiveness can be enhanced by providing transitional processes and supports, development, and mentoring as well as facilitating the development of communities of peers.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".