Simulation in the clinical setting: towards a standard lexicon
Bibliographic record
Abstract
Simulation-based educational activities are happening in the clinical environment but are not all uniform in terms of their objectives, delivery, or outputs. While these activities all provide an opportunity for individual and team training, nuances in the location, timing, notification, and participants impact the potential outcomes of these sessions and objectives achieved. In light of this, there are actually many different types of simulation-based activity that occur in the clinical environment, which has previously all been grouped together as "in situ" simulation. However, what truly defines in situ simulation is how the clinical environment responds in its' natural state, including the personnel, equipment, and systems responsible for care in that environment. Beyond individual and team skill sets, there are threats to patient safety or quality patient care that result from challenges with equipment, processes, or system breakdowns. These have been labeled "latent safety threats." We submit that the opportunity for discovery of latent safety threats is what defines in situ simulation and truly differentiates it from what would be more rightfully called "on-site" simulation. The distinction between the two is highlighted in this article, as well as some of the various sub-types of in situ simulation.
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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 | 0.035 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".