Leadership Development in Postgraduate Medical Education: A Systematic Review of the Literature
Bibliographic record
Abstract
PURPOSE: To evaluate and interpret evidence relevant to leadership curricula in postgraduate medical education (PGME) to better understand leadership development in residency training. METHOD: The authors conducted a systematic review of peer-reviewed, English-language articles from four databases published between 1980 and May 2, 2017 that describe specific interventions aimed at leadership development. They characterized the educational setting, curricular format, learner level, instructor type, pedagogical methods, conceptual leadership framework (including intervention domain), and evaluation outcomes. They used Kirkpatrick effectiveness scores and Best Evidence in Medical Education (BEME) Quality of Evidence scores to assess the quality of the interventions. RESULTS: Twenty-one articles met inclusion criteria. The classroom setting was the most common educational setting (described in 17 articles). Most curricula (described in 13 articles) were isolated, with all curricula ranging from three hours to five years. The most common instructor type was clinical faculty (13 articles). The most commonly used pedagogical method was small group/discussion, followed by didactic teaching (described in, respectively, 15 and 14 articles). Study authors evaluated both pre/post surveys of participant perceptions (n = 7) and just postintervention surveys (n = 10). The average Kirkpatrick Effectiveness score was 1.0. The average BEME Quality of Evidence score was 2. CONCLUSIONS: The results revealed that interventions for developing leadership during PGME lack grounding conceptual leadership frameworks, provide poor evaluation outcomes, and focus primarily on cognitive leadership domains. Medical educators should design future leadership interventions grounded in established conceptual frameworks and pursue a comprehensive approach that includes character development and emotional intelligence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".