Impact of TeamSTEPPS in Intensive Care Units (ICU-STEPPS)
Bibliographic record
Abstract
Objective: Positive safety culture is an essential part of successful transformative change of medical care fields. The Agency for Healthcare Quality (AHRQ) has developed TeamSTEPPS to enhance patient safety and communication. The aim of this article was to identify strengths and challenges in implementing and sustaining a good TeamSTEPPS program. Then, AHRQ survey was applied to assess its effectiveness. Setting: The ICUs of Alexandria Main University Hospital (AMUH).Methods: AHRQ hospital survey was applied before and after implementation process of TeamSTEPPS among 45 ICU residents across all ICUs.Results: Results showed marked development after implementation of TeamSTEPPS in 3 parameters: feedback and communications about errors, handoffs and transitions and frequency of events reported. Good development in communication openness and no punitive response to error.Conclusions: Using TeamSTEPPS in ICUs of AMUH was a successful tool for improving whole safety culture, developing it with continuous monitoring.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".