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Record W4386284220 · doi:10.32920/24050832.v1

Communication of code status escalation for nurses and doctors in the ICU: a case study

2023· preprint· en· W4386284220 on OpenAlexaffabout
Brianna Paddley

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsDocumentationNursingHealth careIntensive care unitMultidisciplinary approachMedicineHealth professionalsIntensive carePsychologyIntensive care medicine

Abstract

fetched live from OpenAlex

Interprofessional team members working in the Intensive Care Unit (ICU) care for patients requiring varying degrees of life sustaining therapy. A patient’s code status can assist healthcare professionals understand the appropriate life support measures to deliver to patients in this setting. Utilizing Stake’s approach to case study research, this study explored the communication processes that occurred between nurses and doctors in an ICU in Toronto, Canada. Participants included nurses and physicians working in the ICU. Data were collected using semi-structured interviews, observations of health care rounds and a chart review. The themes that emerged from this study include: (1) engaging in a multidisciplinary discussion, (2) finding consistent documentation, (3) revisiting the code status, and (4) telling the story. The results of this study elucidate current communication methods to guide future practices on how to effectively disseminate code status decisions amongst interprofessional ICU team members.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0190.006
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.345
GPT teacher head0.527
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes2
Has abstractyes

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