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Record W2032782596 · doi:10.1177/1071181312561165

Crash Cart Drug Drawer Layout and Design

2012· article· en· W2032782596 on OpenAlexaff
Aimee M. Pearson, Jeff K. Caird, Andrew K. Mayer

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Calgary
FundersHealth Research Board
KeywordsCrashCartEmergency departmentMedical emergencyMedicineWorkflowComputer scienceEngineeringNursingDatabase

Abstract

fetched live from OpenAlex

A two-phase study of crash cart medications in the Emergency Department (ED) is reported. The purpose of the phased study was to determine a standardized layout for emergency medications that would promote safety and efficiency during a code blue event. During Phase I, a comprehensive list of ED medications and crash cart configurations was compiled and compared across three acute care hospitals. Three emergency departments were consulted to catalogue medications and to understand the workflow processes surrounding the crash cart such as medication stocking and inventory control. A number of similarities and differences were found for drug usage and crash cart drug drawer layout across hospitals. Phase II examined how ED nurses accessed and used medications from the crash cart. ED nurses individually designed the primary crash cart drawer by placing pictures of each medication into a ‘jigsaw puzzle’ while thinking aloud. Alphabetization of medications and grouping by ABC (airway, breathing and circulation) emerged as primary access strategies proposed by ED nurses. Implications for these and other design insights are discussed with respect to crash cart safety and efficiency.

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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 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

Citations2
Published2012
Admission routes1
Has abstractyes

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