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 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 | 0.101 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".