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The who, what, and how of teamwork research in medical operating rooms: A scoping review

2022· dataset· en· W6902087579 on OpenAlexaff

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsQueen's University
Fundersnot available
KeywordsTeamworkInterpersonal communicationAction (physics)Patient safetyDiversity (politics)Interpersonal relationship

Abstract

fetched live from OpenAlex

Despite the importance of teamwork in the operating room (OR), teamwork can often be conflated with teamwork components (e.g., communication, cooperation). We reviewed the existing literature pertaining to OR teamwork to understand which teamwork components have been assessed. Following PRISMA guidelines for scoping reviews, 4,233 peer-reviewed studies were identified using MEDLINE and Embase. Eighty-seven studies were included for synthesis and analysis. Using the episodic model of teamwork as an organizing framework, studies were grouped into the following teamwork categories: (a) transition processes (e.g., goal specification), (b) action processes (e.g., coordination), (c) interpersonal processes (e.g., conflict management), (d) emergent states (e.g., psychological safety), or (e) omnibus topics (a combination of higher-order teamwork processes). Results demonstrated that action processes were most frequently explored, followed by transition processes, omnibus topics, emergent states, and interpersonal processes. Although all studies were framed as investigations of teamwork, it is important to highlight that most explored only one or a few constructs under the overarching umbrella of teamwork. We advocate for enhanced specificity with descriptions of OR teamwork, reporting practices pertaining to interprofessional demographics and outcomes, and increased diversity in study design and surgery type to advance understanding of teamwork and its implications.

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)0.001
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.143
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0420.000

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 head0.115
GPT teacher head0.414
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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