Exploring the Study of Simulation as a Continuing Professional Development Strategy for Physicians
Bibliographic record
Abstract
INTRODUCTION: Practicing physicians have the responsibility to engage in lifelong learning. Although simulation is an effective experiential educational strategy, physicians seldom select it for continuing professional development (CPD) for reasons that are poorly understood. The objective of this study was to explore existing evidence on simulation-based CPD and the factors influencing physicians' engagement in simulation-based CPD. METHODS: A scoping review of the literature on simulation-based CPD included MEDLINE, Embase, and CINAHL databases. Studies involving the use of simulation for practicing physicians' CPD were included. Information related to motivations for participating in simulation-based CPD, study objectives, research question(s), rationale(s), reasons for using simulation, and simulation features was abstracted. RESULTS: The search yielded 8609 articles, with 6906 articles undergoing title and abstract screening after duplicate removal. Six hundred sixty-one articles underwent full-text screening. Two hundred twenty-five studies (1993-2021) were reviewed for data abstraction. Only four studies explored physicians' motivations directly, while 31 studies described incentives or strategies used to enroll physicians in studies on simulation-based CPD. Most studies focused on leveraging or demonstrating the utility of simulation for CPD. Limited evidence suggests that psychological safety, direct relevance to clinical practice, and familiarity with simulation may promote future engagement. DISCUSSION: Although simulation is an effective experiential educational method, factors explaining its uptake by physicians as a CPD strategy are unclear. Additional evidence of simulation effectiveness may fail to convince physicians to participate in simulation-based CPD unless personal, social, educational, or contextual factors that shape physicians' motivations and choices to engage in simulation-based CPD are explored.
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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 | 0.023 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".