Canadian medical faculty senior leaders: what skills do they need?
Bibliographic record
Abstract
PURPOSE: Many academic leaders have little formal leadership training, which can result in challenges to effective leadership, succession planning and burnout. This paper aims to explore the leadership skills needed to be an effective senior academic leader in a Canadian medical faculty. DESIGN/METHODOLOGY/APPROACH: An anonymous voluntary survey of needed leadership skills and supports was sent to 60 senior academic leaders at the University of Alberta. This was followed by interviewing a purposive sample, using open-ended questions based on a multimodal needs assessment of senior academic leaders. The authors used an iterative process to analyze the data; anonymized transcripts were coded and categorized separately by two researchers, and themes were created. FINDINGS: = 12) were unanimous that they felt unprepared at the start of the leadership role. The survey and interviews identified five major themes for leadership skills: Mentoring, Finances, Human Resources, Building Relationships and Protected Time. Networking and leadership courses were identified as major sources of support. RESEARCH LIMITATIONS/IMPLICATIONS: , 2013). While the survey had a 42% response rate (25/60), the survey responses were echoed in the interviews. Although the purposive sample was small, the interviewed leaders were a representative sample of the larger leadership group. ORIGINALITY/VALUE: Academic leaders may benefit from a mentorship team/community of leaders and specific university governance knowledge which may help their ability to influence and advance their strategic initiatives.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".