A Scoping Review and Realist Synthesis of Surgical Trainees’ Perspectives in Competency-based Training
Bibliographic record
Abstract
OBJECTIVE: To summarize the literature pertaining to surgical trainees' experiences with competency-based medical education (CBME), outline strengths and weaknesses, and synthesize the evidence to map the sequence of activities that must be successfully implemented for CBME to function effectively. SUMMARY BACKGROUND DATA: CBME has seen rapid and widespread adoption in surgical training programs globally. Amid reports of mixed findings for how CBME programs impact trainees' wellness, program directors require evidence-informed solutions. METHODS: A scoping literature review of Medline, Embase, ERIC, PsycINFO, and the grey literature was conducted between January 1, 2012 and August 9, 2024 for studies reporting surgical trainees' perceptions of advantages and disadvantages of CBME. The data was synthesized with reflexive thematic analysis, and from a scientific realism perspective to generate a conceptual map of how themes relate to the intended program theory of CBME. RESULTS: 2160 titles were identified. After title and abstract screening, 1933 studies were excluded, leaving 227 articles for full-text review of which 213 were excluded. Reported advantages and disadvantages of CBME organized into four themes: logistics of implementation, educational value, infectious apathy, and psychological implications. A series of program theories are proposed and mapped to the themes, providing a contextualized understanding of how the intended impact of CBME on trainees has differed from reality. CONCLUSIONS: By mapping themes and proposing related program theories, we provide surgical programs in different contexts a guide to fine-tune ongoing CBME implementation processes. We believe such refinements will enhance resident well-being, while promoting the goal of creating safe, capable surgeons.
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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 | 0.083 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.034 | 0.036 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".