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Record W2414525090 · doi:10.3233/978-1-58603-979-0-186

Understanding the Impact on Intensive Care Staff Workflow Due to the Introduction of a Critical Care Information System: A Mixed Methods Research Methodology

2009· article· en· W2414525090 on OpenAlex
Nicola Shaw, Rebecca L. Mador, Sai Yin Ho, Damon C. Mayes, Johanna Westbrook, Nerida Creswick, K Thiru, Melissa Brown

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueStudies in health technology and informatics · 2009
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWorkflowIntensive care unitHealth careMedicineMechanical ventilatorIntensive careMedical emergencyNursingComputer scienceIntensive care medicineMechanical ventilationDatabase

Abstract

fetched live from OpenAlex

The Intensive Care Unit (ICU) is a complex and dynamic tertiary care environment that requires health care providers to balance many competing tasks and responsibilities. Inefficient and interruption-driven workflow is believed to increase the likelihood of medical errors and, therefore, present a serious risk to patients in the ICU. The introduction of a Critical Care Information System (CCIS), is purported to result in fewer medical errors and better patient care by streamlining workflow. Little objective research, however, has investigated these assertions. This paper reports on the design of a research methodology to explore the impact of a CCIS on the workflow of Respiratory Therapists, Pediatric Intensivists, Nurses, and Unit Clerks in a Pediatric ICU (PICU) and a General Systems ICU (GSICU) in Northern Canada.

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.

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)0.004
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
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 head0.411
GPT teacher head0.563
Teacher spread0.152 · 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