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Record W2344913211 · doi:10.1177/1049732315609901

Communication and Culture in the Surgical Intensive Care Unit

2015· article· en· W2344913211 on OpenAlexaffabout
Lesley Gotlib Conn, Barbara Haas, Brian H. Cuthbertson, André Carlos Kajdacsy-Balla Amaral, Natalie G. Coburn, Avery B. Nathens

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsNursingProfessional boundariesMedicineIntensive careDisciplineIntensive care unitPerceptionQualitative researchEthnographyQuality (philosophy)PsychologyIntensive care medicineSociology

Abstract

fetched live from OpenAlex

This ethnography explores communication around critically ill surgical patients in three surgical intensive care units (ICUs) in Canada. A boundary framework is used to articulate how surgeons', intensivists', and nurses' communication practices shape and are shaped by their respective disciplinary perspectives and experiences. Through 50 hours of observations and 43 interviews, these health care providers are found to engage in seven communication behaviors that either mitigate or magnify three contested symbolic boundaries: expertise, patient ownership, and decisional authority. Where these boundaries are successfully mitigated, experiences of collaborative, high-quality patient care are produced; by contrast, boundary magnification produces conflict and perceptions of unsafe patient care. Findings reveal that high quality and safe patient care are produced through complex social and cultural interactions among surgeons, intensivists, and nurses that are also expressions of knowledge and power. This enhances our understanding of why current quality improvement efforts targeting communication may be ineffective.

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)0.015
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
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 head0.898
GPT teacher head0.719
Teacher spread0.179 · 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 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

Citations34
Published2015
Admission routes2
Has abstractyes

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