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Record W2033924181 · doi:10.1097/mcc.0b013e32833e9731

Team working in intensive care: current evidence and future endeavors

2010· review· en· W2033924181 on OpenAlexaff
Joanne Richardson, Michael West, Brian H. Cuthbertson

Bibliographic record

VenueCurrent Opinion in Critical Care · 2010
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCurrent (fluid)Intensive careIntensive care medicineMEDLINE

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.352
GPT teacher head0.615
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2010
Admission routes1
Has abstractyes

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