Training and simulation for patient safety
Bibliographic record
Abstract
BACKGROUND: Simulation-based medical education enables knowledge, skills and attitudes to be acquired for all healthcare professionals in a safe, educationally orientated and efficient manner. Procedure-based skills, communication, leadership and team working can be learnt, be measured and have the potential to be used as a mode of certification to become an independent practitioner. RESULTS: Simulation-based training initially began with life-like manikins and now encompasses an entire range of systems, from synthetic models through to high fidelity simulation suites. These models can also be used for training in new technologies, for the application of existing technologies to new environments and in prototype testing. The level of simulation must be appropriate to the learners' needs and can range from focused tuition to mass trauma scenarios. The development of simulation centres is a global phenomenon which should be encouraged, although the facilities should be used within appropriate curricula that are methodologically sound and cost-effective. DISCUSSION: A review of current techniques reveals that simulation can successfully promote the competencies of medical expert, communicator and collaborator. Further work is required to develop the exact role of simulation as a training mechanism for scholarly skills, professionalism, management and health advocacy.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".