A stakeholder‐based approach to leadership development training: the case of medical education in Canada
Bibliographic record
Abstract
This paper reports the use of a stakeholder‐based, bottom‐up approach to determining leadership training needs and designing leadership training programs which contrasts with the top‐down policy that is often applied. The context is a Canadian medical school. Leadership training in medicine is in its infancy. Discussed and outlined in this study are stakeholder opinions on the pillars and priorities upholding leadership development. Seventy‐seven semi‐structured interviews were conducted with stakeholder groups including Trainees, Mid‐Level University Leaders, Senior Medical Clinician Leaders, Senior University Leaders, Medical Scientists and Senior Executives and Directors external to the University. Conventional content analysis was used to group the text into common themes and then group the themes into more general categories. Five general categories emerged: formal programming, organizational support, program evaluation, skill development and participant selection. A successful training program starts with identifying the individuals most likely to benefit from such a program. It emphasizes early training, mentorship and experiential learning. Programs should focus on developing: self‐awareness, communication skills and team‐building capability. Formal training programs would be most successful if they were regularly evaluated and strongly supported by their parent organizations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.046 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".