Team working in intensive care: current evidence and future endeavors
Bibliographic record
Abstract
PURPOSE OF REVIEW: It has recently been argued that the future of intensive care medicine will rely on high quality management and teamwork. Therefore, this review takes an organizational psychology perspective to examine the most recent research on the relationship between teamwork, care processes, and patient outcomes in intensive care. RECENT FINDINGS: Interdisciplinary communication within a team is crucial for the development of negotiated shared treatment goals and short-team patient outcomes. Interventions for maximizing team communication have received substantial interest in recent literature. Intensive care coordination is not a linear process, and intensive care teams often fail to discuss how to implement goals, trigger and align activities, or reflect on their performance. Despite a move toward interdisciplinary team working, clinical decision-making is still problematic and continues to be perceived as a top-down and authoritative process. The topic of team leadership in intensive care is underexplored and requires further research. SUMMARY: Based on findings from the most recent research evidence in medicine and management, four principles are identified for improving the effectiveness of team working in intensive care: engender professional efficacy, create stable teams and leaders, develop trust and participative safety, and enable frequent team reflexivity.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".