20 The masterclass series in family doctor leadership: evaluation of a new approach to leadership development
Bibliographic record
Abstract
Context Leadership is essential for quality improvement in family medicine. Objective To assess whether the Master Class approach to developing ‘rising stars’ in performing arts is effective in developing emerging leaders in academic family medicine. Design Mixed Methods, combining quantitative evaluation of five sessions and qualitative assessment of participants’ pre-course assignments and post-course interviews. Setting The Department of Family & Community Medicine (DFCM) at the University of Toronto, comprising 14 academic sites, multiple community practices and over 1,700 faculty.Participants: Sixteen ‘rising star’ DFCM leaders, identified by site Chiefs and Program Directors. Intervention Five 2-hour evening sessions over ten weeks, each conducted by a different DFCM facilitator with internationally recognized leadership in varied domains. Outcome measures Qualitative assessment of pre-course descriptions by participants of one of their current challenges, quantitative ratings of each session and qualitative assessment of impact on participants. The problem descriptions and interviews were assessed using descriptive thematic analysis. Results The participants’ descriptions of their leadership challenges revealed significant variation in level of complexity, scope, and framing of the issues. Evaluations of individual sessions were uniformly high, yielding a combined average of all elements of 4.72/5. Analysis of participant interviews at 2–4 months post-course revealed the following themes: impact or potential for impact on their work; most effective aspects; least effective aspects; participant expectations; suggestions for improvement; impact on self-perception as leaders; broader perceptions of leadership approaches; and acquisition of specific skills. Conclusion The Master Class approach can be adapted to developing rising leaders in family medicine and may be broadly applicable to healthcare leadership development.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".