Application of the METRICS Framework to Build Programs of Healthcare Simulation Research
Bibliographic record
Abstract
The evidence base supporting the adoption of simulation in health care has not kept pace with the rapid growth of the field. Although there is a growing body of research in health care simulation, many published studies describe small-scaled, underpowered projects with insufficient methodological rigor to inform our understanding of simulation. This problem is indicative of a larger challenge: the lack of focused, cohesive programs of research designed to advance the science of simulation. The METRICS framework is a model of scholarship that categorizes scholarship into 7 intersecting domains: Metascholarship, Evaluation, Translation, Research, Innovation, Conceptual, and Synthesis. In this article, we aim to explore how the METRICS framework can serve as a roadmap for researchers to develop cohesive simulation research programs. We also describe how the METRICS framework applies to existing institutional and network-based programs of health care simulation research and discuss future implications for the global health care simulation community.
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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 | 0.134 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| 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".