Simulation research to enhance patient safety and outcomes: recommendations of the Simnovate Patient Safety Domain Group
Bibliographic record
Abstract
The use of simulation-based training has established itself in healthcare but its implementation has been varied and mostly limited to technical and non-technical skills training. This article discusses the possibilities of the use of simulation as part of an overarching approach to improving patient safety, and represents the views of the Simnovate Patient Safety Domain Group, an international multidisciplinary expert group dedicated to the improvement of patient safety. The application and integration of simulation into the various facets of a learning healthcare system is discussed, with reference to relevant literature and the different modalities of simulation which may be employed. The selection and standardisation of outcomes is highlighted as a key goal if the evidence base for simulation-based patient safety interventions is to be strengthened. This may be achieved through the establishment of standardised reporting criteria. If such safety interventions can be proven to be effective, financial incentives are likely to be necessary to promote their uptake, with the intention that up-front cost to payers or insurers be recouped in the longer term but reductions in complications and lengths of stay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.175 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".