Education and simulation techniques for improving reliability of care
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.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".