Leadership development programs for surgical residents: A narrative review of the literature
Bibliographic record
Abstract
While North American accreditation bodies have included leadership as a core competency for all clinicians, there remains a general lack of strategy and evidence on how surgical residents are expected to achieve that objective. This paper aimed to systematically review the current body of literature on leadership development programs (LDPs) for surgical residents. Articles pertaining to LDPs for surgical residents were identified through an electronic database search including Medline and EMBASE. Each LDP was stratified by setting, frequency, content, teaching methods, outcomes, and cross-referenced against national accreditation competencies. The Kirkpatrick model and Best Evidence Medical Education (BEME) scale were used to assess curriculum effectiveness and quality, respectively. Nine articles were selected for final review. Despite significant content variability, the most common topics included leadership theory (89%), and team building/ management (56%). Reported learning outcomes, measured primarily via surveys, included an improvement in understanding leadership (n = 4), communication skills (n = 3), and team building/management skills (n = 3). The overall effectiveness of each program was low, with 67% having a Kirkpatrick effectiveness score of 1, indicating only a change in learners’ attitudes. The highest BEME score, achieved by 56% of programs, was 3/5 (i.e., conclusions can probably be based on the results). Only 33% of studies (n = 3) framed outcomes in the context of national accreditation competencies. The current body of literature on leadership curricula for surgical residents is heterogeneous and limited in effectiveness and quality. Future programs should be rooted in leadership theory and national accreditation competencies.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".