Problem Identification and Needs Assessment for a Universal Surgical Simulation Educational Fellowship Curriculum
Bibliographic record
Abstract
OBJECTIVE: Simulation-based training is a critical adjunct to clinical medicine, and it has grown exponentially in academic and community healthcare settings in recent decades. The expansion is multifactorial, and the proliferation has resulted in the need for well-informed, well-trained simulation educators and leaders. The increased demand has driven the growth of accredited surgical simulation fellowships and formal surgical education qualifications. However, there are currently no standardized curricula for these fellowship programs. We aimed to perform a problem identification and needs assessment to develop a surgical simulation fellow curriculum. METHODS AND DESIGN: A mixed-methods needs assessment was performed. Problem identification and general needs assessment were conducted during regular Association of Surgical Education (ASE) Simulation Committee meetings. The general needs assessment involved a scoping literature review to identify papers on existing longitudinal simulation curricula, with searches of PubMed, EMBASE, and Web of Science. The targeted needs assessment involved a focus group interview of surgical simulation fellows, and the investigative team performed a thematic analysis. RESULTS: Key stakeholders of surgical simulation fellowships identified no standard curriculum for these fellows. They identified that a collaborative national project to develop a curriculum accessible to surgical simulation fellows could enhance the learning experience. The scoping literature review identified 258 studies, 7 full articles, and 3 articles meeting inclusion criteria, demonstrating a paucity of literature on curricula for simulation educational fellows. The general and targeted needs assessment informed the development of suggested modules for the surgical simulation curriculum, and the first draft of proposed modules was discussed with the ASE Board; feedback was incorporated, and a final list of modules was produced. CONCLUSIONS: Through Kern's steps of problem identification and needs assessment, we describe the structure for a novel national innovative curriculum to educate surgical simulation fellows and those interested in surgical simulation education.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".