An evidence-based practice project for recognition of clinical deterioration: Utilization of simulation-based education
Bibliographic record
Abstract
Background: As more complex patients are hospitalized, the need for highly skilled and competent nurses to recognize clinical deterioration becomes more apparent. The literature supports the use of simulation-based education to enhance the recognition of clinical deterioration. The purpose of this evidence-based practice project was to utilize simulation as an educational modality to improve the knowledge of registered nurses in the recognition of clinical deterioration among their patients. Methods: This evidence-based practice project was conducted from May through June 2013 in a 900-bed facility. Participation was voluntary and included 15 medical-surgical, procedural, and post-anesthesia care unit registered nurses. Simulation-based education was utilized for assessing the recognition, management, and reporting of clinical deterioration by nurses while supporting learning in a safe environment. Each participant managed two simulated patients in deteriorating states. Baseline performance was obtained during the initial simulation scenario by utilizing RAPIDS, a validated tool that evaluates assessment, management, and clinical deterioration reporting. A post-simulation debriefing and education session occurred that included a review of all required critical action elements. Debriefing was followed by a second post-intervention simulated clinical deterioration scenario. Results: The results indicated statistically significant improvement in mean assessment and management scores when the post-intervention results were compared with baseline [ t (14) =2.04, p = .03]. Post-intervention reporting scores were also improved, although this change was not statistically significant. Conclusions: Simulation-based education may be an effective strategy for impacting a nurse’s ability to recognize clinical deterioration and thereby allow for timely intervention.
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How this classification was reachedexpand
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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".