Exploring intensive care nurses’ team performance in a simulation-based emergency situation, − expert raters’ assessments versus self-assessments: an explorative study
Bibliographic record
Abstract
BACKGROUND: Effective teamwork has proven to be crucial for providing safe care. The performance of emergencies in general and cardiac arrest situations in particular, has been criticized for primarily focusing on the individual's technical skills and too little on the teams' performance of non-technical skills. The aim of the study was to explore intensive care nurses' team performance in a simulation-based emergency situation by using expert raters' assessments and nurses' self-assessments in relation to different intensive care specialties. METHODS: The study used an explorative design based on laboratory high-fidelity simulation. Fifty-three registered nurses, who were allocated into 11 teams representing two intensive care specialties, participated in a videotaped simulation-based cardiac arrest setting. The expert raters used the Ottawa Crisis Resource Management Global Rating Scale and the first part of the Mayo High Performance Teamwork Scale to assess the teams' performance. The registered nurses used the first part of the Mayo High Performance Teamwork Scale for their self-assessments, and the analyses used were Chi-square tests, Mann-Whitney U tests, Spearman's rho and Intraclass Correlation Coefficient Type III. RESULTS: The expert raters assessed the teams' performance as either advanced novice or competent, with significant differences being found between the teams from different specialties. Significant differences were found between the expert raters' assessments and the registered nurses' self-assessments. CONCLUSIONS: Teams of registered nurses representing specialties with coronary patients exhibit a higher competence in non-technical skills compared to team performance regarding a simulated cardiac arrest. The use of expert raters' assessments and registered nurses' self-assessments are useful in raising awareness of team performance with regard to patient safety.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".