Education and simulation techniques for improving reliability of care
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
PURPOSE OF REVIEW: Multiple factors influence the dependability of intensive care provision. The management of a group of unstable, critically ill patients requires focused attention from the clinical team. Medical simulation is an important tool to improve safety and team work within the ICU. RECENT FINDINGS: The critical care healthcare team needs to work both individually and together in such a way as to optimise patient care and prevent error. This involves nontechnical skills including decision making, task allocation, team working and situation awareness, all of which are underpinned by communication, cooperation and coordination. The use of integrated simulators to create realistic patient scenarios with structured debriefing is an excellent method for teaching in these domains. There has been a huge increase in the delivery of training and education using an expanding variety of clinical simulators. SUMMARY: This review summarises the evidence and opinion about how simulation tools can be optimally used. In addition, we propose an educational strategy to optimise the impact on clinical practice by embedding simulation training in a multidisciplinary teaching programme based upon a specifically developed curriculum focusing on the teaching of crisis resource management and patient safety.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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