Leadership Development Programs for Radiology Residents: A Literature Review
Bibliographic record
Abstract
PURPOSE: Leadership development has become increasingly important in medical education, including postgraduate training in the specialty of radiology. Since leadership skills may be acquired, there is a need to establish leadership education in radiology residency training. However, there is a paucity of literature examining the design, delivery, and evaluation of such programs. The purpose of this study is to collate and characterize leadership training programs across postgraduate radiology residencies found in the literature. METHODS: A scoping review was conducted. Relevant articles were identified through a search of Ovid MEDLINE, Ovid EMBASE, Cochrane, PubMed, Scopus, and ERIC databases from inception until June 22, 2020. English-language studies characterizing leadership training programs offered during postgraduate radiology residency were included. A search of the grey literature was completed via a web-based search for target programs within North America. RESULTS: The literature search yielded 1168 citations, with 6 studies meeting inclusion criteria. Four studies were prospective case series and two were retrospective. There was heterogeneity regarding program structure, content, teaching methodology, and evaluation design. All programs were located in the United States. Outcome metrics and success of the programs was variably reported, with a mix of online and in person feedback used. The grey literature search revealed 3 American-based programs specifically catered to radiology residents, and none within Canada. CONCLUSION: The review highlighted a paucity of published literature describing leadership development efforts within radiology residency programs. The heterogeneity of programs highlighted the need for guidance from regulatory bodies regarding delivery of leadership curricula.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".