Living, Leading & Medicine: A two‐tiered leadership development programme for family medicine residents
Bibliographic record
Abstract
BACKGROUND: There is increasing awareness of the necessity and importance for physician leadership in health care. Despite this, formal leadership training is not widespread in medical education. APPROACH: We describe the structure, curriculum and development of a robust two-tiered leadership development programme within a community-based family medicine residency programme. Living, Leading & Medicine (LLM, tier 1) consists of nine 2.5-h discussion-based training sessions occurring thrice annually. The Advanced Leadership Track (ALT, tier 2) includes mentoring, additional readings, personal evaluations and leadership projects. EVALUATION: We used post-session surveys and exit surveys for LLM and ALT, respectively. We utilised the modified Kirkpatrick framework for programme evaluation to present outcomes from the first 3 years for each tier. Over three quarters (40 out of 53) of residents participated in LLM sessions. The post-session survey response rate for LLM was 95% (124 out of 130 participants). Eighteen out of 33 residents (54.5%) completed the ALT. Of these, 72% (13 of 18) returned exit surveys. Residents found the programme valuable and relevant (Kirkpatrick level 1). Residents demonstrated improvements in leadership knowledge and skills (3.85 v. 3.11, p < 0.0001; Kirkpatrick level 2) compared with an internal, historic control group. We noted changes in resident behaviour and attitudes towards leadership (Kirkpatrick level 3). Finally, the completion of leadership projects demonstrates Kirkpatrick level 4 outcomes. IMPLICATIONS: We have created a longitudinal, two-tiered leadership development programme that has improved the leadership capabilities of our family medicine residents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".