A Master Class in Family Doctor Leadership: Evaluating an Innovative Program
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In family medicine, leadership is critical for health care delivery, advancing curricula, research, and quality improvement. Systematic reviews of leadership development programs in health care identify limitations, calling for innovative designs and rigorous assessment. Our objective was to evaluate the impact of applying master class principles to leadership development in academic family medicine. METHODS: We used mixed methods to assess the impact of an innovative master class program on 15 emerging leaders in a large academic department of family medicine. The program consisted of five sessions where family physician masters shared their wisdom, techniques, and feedback with promising leaders. Quantitative evaluation involved participants' ratings of each session's content and delivery using a 5-point Likert scale. We assessed postcourse semistructured interviews with participants qualitatively using descriptive thematic content analysis. RESULTS: Individual sessions were highly evaluated, with a combined mean of 4.82/5. Qualitative thematic analysis identified self-perceived increased effectiveness in leadership activities; increased confidence as a leader; increased motivation to be a leader; and perceptions of value from the program, contributing to what participants described as unexpected potential change within themselves. Themes related to effectiveness of the program were practical advice; networking; diverse topics; accessible speakers sharing personal stories; and small-group, informal, early-evening format. CONCLUSIONS: Master class concepts can be adapted to leadership development in academic family medicine, with evidence of early positive impact on participants' self-perception of leadership skills and confidence. Further research is warranted to assess organizational impact and applicability to other settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".