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Record W2007566158 · doi:10.1177/154193120805201207

Identification of Intensive Care Unit (ICU) System Integration Conflicts: Evaluation of Two Mock-up Rooms Using Patient Simulation

2008· article· en· W2007566158 on OpenAlexaffabout
Susan Chisholm, Jonas Shultz, Jeff K. Caird, Jason Lord, Paul Boiteau, Jan M. Davies

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2008
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsDebriefingUsabilityIntensive care unitIdentification (biology)Medical emergencyIntensive carePresentation (obstetrics)Think aloud protocolMedicinePsychologyComputer scienceHuman–computer interactionIntensive care medicineMedical education

Abstract

fetched live from OpenAlex

To address increasing patient demands and acuity, the Calgary Health Region is renovating the intensive care units (ICU) at three of their adult acute care sites. Before finalizing the design plans, mock-up rooms were created at two of the sites according to several proposed room designs in order to identify potential issues during the design phase of the project. All necessary equipment was included within each of the two mock-up rooms so as to nearly replicate a functioning ICU. Evaluations of equipment, room layout and conflicts were accomplished using patient simulation of a cardiac arrest, an acutely ill patient, a palliative care patient and the admission of a new patient. Digital videos, think aloud audio tracks and extensive debriefing sessions were combined and analyzed. Specific category issues were identified including the articulating arms, visibility of the patient monitors, equipment usability, collisions with equipment, and communication issues. Elaboration of each issue and presentation of design recommendations is given.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.099
GPT teacher head0.357
Teacher spread0.258 · 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 designObservational
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

Citations14
Published2008
Admission routes2
Has abstractyes

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