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Record W2153548002 · doi:10.1186/cc10353

Teamwork and team training in the ICU: Where do the similarities with aviation end?

2011· article· en· W2153548002 on OpenAlexaff
Tom W. Reader, Brian H. Cuthbertson

Bibliographic record

VenueCritical Care · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsTeamworkAviationMedicineMultidisciplinary approachConceptualizationIntensive care unitPsychological interventionWork (physics)Crew resource managementMedical educationNursingMedical emergencyEngineeringIntensive care medicineComputer science

Abstract

fetched live from OpenAlex

The aviation industry has made significant progress in identifying the skills and behaviors that result in effective teamwork. Its conceptualization of teamwork, development of training programs, and design of assessment tools are highly relevant to the intensive care unit (ICU). Team skills are important for maintaining safety in both domains, as multidisciplinary teams must work effectively under highly complex, stressful, and uncertain conditions. However, there are substantial differences in the nature of work and structure of teams in the ICU in comparison with those in aviation. While intensive care medicine may wish to use the advances made by the aviation industry for conceptualizing team skills and implementing team training programs, interventions must be tailored to the highly specific demands of the ICU.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.143
GPT teacher head0.408
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations48
Published2011
Admission routes1
Has abstractyes

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