The Impact of Leadership Training Programs on Physicians in Academic Medical Centers
Bibliographic record
Abstract
PURPOSE: To identify the impact of leadership training programs at academic medical centers (AMCs) on physicians' knowledge, skills, attitudes, behaviors, and outcomes. METHOD: In 2011, the authors conducted a systematic review of the literature, identifying relevant studies by searching electronic databases (MEDLINE, EMBASE, CINAHL, Cochrane Central Register), scanning reference lists, and consulting experts. They deemed eligible any qualitative or quantitative study reporting on the implementation and evaluation of a leadership program for physicians in AMCs. Two independent reviewers conducted the review, screening studies, abstracting data, and assessing quality. RESULTS: The authors initially identified 2,310 citations. After the screening process, they had 11 articles describing 10 studies. Three were controlled before-and-after studies, four were before-and-after case series, and three were cross-sectional surveys. The authors did not conduct a meta-analysis because of the methodological heterogeneity across studies. Although all studies were at substantial risk of bias, the highest-quality ones showed that leadership training programs affected participants' advancement in academic rank (48% versus 21%, P=.005) and hospital leadership position (30% versus 9%, P=.008) and that participants were more successful in publishing papers (3.5 per year versus 2.1 per year, P<.001) compared with nonparticipants. CONCLUSIONS: The authors concluded that leadership programs have modest effects on outcomes important to AMCs. Given AMCs' substantial investment in these programs, rigorous evaluation of their impact is essential. High-quality studies, including qualitative research, will allow the community to identify which programs are most effective.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".