Millennium Conference 2005 on Medical Simulation: A Summary Report
Bibliographic record
Abstract
INTRODUCTION: Medical simulation takes advantage of contextual and experiential learning by allowing trainees to practice in realistic environments prior to actual patient care. Although proponents argue that patient simulation can fundamentally enhance both medical education and patient safety, large-scale experience with advanced simulation technologies is limited. To explore expert opinion on the topic, we convened a conference of educational leaders and simulation experts to provide recommendations for how this field should be directed on a broad scale to improve the training of future health professionals. This document summarizes the proceedings of that conference. METHODS: We issued a request for applications to all U.S. and Canadian medical schools within the Association of American Medical Colleges (AAMC), seeking a diverse group of institutional teams committed to an in-depth exploration of the topic. Of 33 applications, nine medical schools were selected to participate. Once on site, eight working groups were formed, each comprised of representatives across sites and roles, including deans, clerkship and program directors, content experts, and trainees. We addressed four key topics, which are subsequently summarized for presentation in this report: 1) education (How can medical simulation contribute to the education of trainees?), 2) assessment (What is the role of simulation in evaluating trainees in the context of general competencies?), 3) research (How should we develop a research agenda to evaluate simulation?), and 4) implementation (How should simulation technologies be developed and managed within and across institutions?). RESULTS: Participants in the conference generally agreed that simulation offers a conducive environment for focused reflection and critical thought. Although there was consensus that medical simulation can provide a robust platform for performance assessment, most participants thought that the research basis for high-stakes assessment was still too immature for widespread implementation. Participants generally agreed that sufficiently powered research will require interinstitutional collaboration on uniform curricula and meaningful outcome tools, and that both biomedical and social science research paradigms will need to be applied to the questions at hand. Common barriers to medical simulation include both real and perceived lack of resources, poor understanding among faculty regarding the nature of the tools and techniques, and the inherent complexity of multidisciplinary collaboration. CONCLUSIONS: Medical simulation can and should be used to complement current methods of medical education. Educators should make thoughtful choices among simulation modalities to help trainees most effectively achieve learning objectives. Simulation researchers should prioritize the development and validation of clinical performance tools and other defined outcome measures on which meaningful large-scale research can be anchored. Finally, national collaboration should be encouraged and fostered by institutions and funding agencies.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.034 |
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".