Training and simulation for patient safety
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it