Talking the talk and walking the walk: Are patient safety priorities addressed by simulation-based education?
Bibliographic record
Abstract
Background \nOver the last decade healthcare simulation has received strong government support in Australia and New Zealand e.g. in 2013 the Australian Government invested $46 million in capital and $48 million in recurrent funding in the establishment of over 200 simulation centres. The key driver for simulation-based education is to improve the quality and safety of healthcare and both countries have evidence-based, national measures of patient safety1,2. While simulation-based education has had a significant impact on knowledge and skills, there is limited evidence of the transfer of learning to practice and improved patient outcomes. \n \nObjective \nTo explore the extent to which simulation-based education addresses the contemporary patient safety priorities in Australia1 and New Zealand2. \n \nMethods \nA scoping review of literature published between 2007-2016. Primary studies were included if they related to the NSQHS Standards1/HQS Indicators2 and evaluated the impact on clinician behaviour or patient safety. Studies were tabulated and synthesised. \n \nResults \nWe identified 15 studies for inclusion in the review. Nine studies were undertaken in the USA, two in the UK and one each from Canada, France, Israel and Australia. Most studies were of medical and nursing staff, and five were interdisciplinary. In 11 of the studies the sample size was <40. Only four of the ten NSQHS standards (Preventing and controlling health care associated infections, medication safety, Patient handover and Recognising and responding to clinical deterioration in acute care) were addressed. \n \nConclusion \nCritical synthesis of the literature identified that simulation-based education research addressed a limited number of quality and safety standards, with an emerging body of international research demonstrating significant impact on clinician behaviours and patient outcomes. The dearth of evidence from Australia and New Zealand suggests that outcomes of simulation in this region are not yet commensurate with significant investments that have been made. \n \n \nReferences \n1. Australian Commission on Safety and Quality in Healthcare (2012) Retrieved 6th January 2017 from National Safety and Quality Health Service (NSQHS). \n2. Health Quality and Safety Commission New Zealand. (2016) Retrieved 20th Jan 2017 from Health quality & safety indicators.
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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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".