The impact of web-based and face-to-face simulation on patient deterioration and patient safety: protocol for a multi-site multi-method design
Bibliographic record
Abstract
BACKGROUND: There are international concerns in relation to the management of patient deterioration which has led to a body of evidence known as the 'failure to rescue' literature. Nursing staff are known to miss cues of deterioration and often fail to call for assistance. Medical Emergency Teams (Rapid Response Teams) do improve the management of acutely deteriorating patients, but first responders need the requisite skills to impact on patient safety. METHODS/DESIGN: In this study we aim to address these issues in a mixed methods interventional trial with the objective of measuring and comparing the cost and clinical impact of face-to-face and web-based simulation programs on the management of patient deterioration and related patient outcomes. The education programs, known as 'FIRST(2)ACT', have been found to have an impact on education and will be tested in four hospitals in the State of Victoria, Australia. Nursing staff will be trained in primary (the first 8 min) responses to emergencies in two medical wards using a face-to-face approach and in two medical wards using a web-based version FIRST(2)ACTWeb. The impact of these interventions will be determined through quantitative and qualitative approaches, cost analyses and patient notes review (time series analyses) to measure quality of care and patient outcomes. DISCUSSION: In this 18 month study it is hypothesised that both simulation programs will improve the detection and management of deteriorating patients but that the web-based program will have lower total costs. The study will also add to our overall understanding of the utility of simulation approaches in the preparation of nurses working in hospital wards. (ACTRN12616000468426, retrospectively registered 8.4.2016).
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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 | 0.088 | 0.101 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".