Leadership training in family medicine residency: a scoping review
Bibliographic record
Abstract
Background Graduate medical education, including family medicine residency, has historically focused on building clinical competencies with little attention paid to leadership skills, leaving residents feeling ill-prepared for leadership roles after training. Objective To analyse the format, content and outcomes of leadership training programmes offered to family medicine residents. Methods A MEDLINE (OvidSP) literature search from 1976 to October 2018 for articles on Family Medicine AND Residency AND Leadership Programs retrieved 184 articles. After reviewing inclusion and exclusion criteria, 12 articles were chosen for full review and synthesis. Results Three articles described leadership training available to Family Medicine all residents while nine focused on a select group. Programme format and content varied, ranging from a 1-day programme on emotional intelligence to a 5-year integrated leadership track. The most comprehensive curricula were longitudinal and offered to a small group of residents. Inclusive programmes often taught leadership through the lens of a specific competency. Mixed teaching methods were valued including online learning, simulations, small group discussions, mentorship, reflection, placements and projects. Conceptual frameworks were inconsistently used and programme evaluation seldom addressed high-level or long-term outcomes. Conclusions Leadership skills are important for all family physicians; however, there is limited literature on comprehensive leadership development during training. Existing curricula were described in this review and we suggest a longitudinal mixed-methods programme integrated throughout residency, covering basic comprehensive skills for all residents. However, evaluative data were limited, and a considerable gap remains in how to effectively approach leadership development in family medicine residency, warranting ongoing research.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".