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Record W162342618

Applications of ecological interface design in supporting the nursing process.

2004· article· en· W162342618 on OpenAlex

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.

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWorkloadInterface (matter)Process (computing)Computer scienceNursing processDecision support systemProcess managementControl (management)NursingHuman–computer interactionMedicineBusinessData mining
DOInot available

Abstract

fetched live from OpenAlex

Today's nursing environment is complex, with many sources of data that are often poorly displayed. Ecological interface design (EID) is a systematic approach to designing interfaces to complex systems. EID has been used to design interfaces for aviation displays, power plant monitoring and control, human hemodynamic monitoring, anesthesia monitoring, and neonatal intensive care monitoring and diagnosis. EID makes critical relationships easily visible, eliminating the mental workload of integrating, calculating, or remembering multiple values. This paper reviews past experimental studies of EID in healthcare applications and discusses the application of EID to a decision-support tool for diabetic patients using personal digital assistants. The authors also discuss other contributions that EID could make to the nursing process in the areas of physiological monitoring, decision support, database design, and the measurement and analysis of nurse-sensitive outcomes, including patient safety outcomes.

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)0.000
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 score0.850
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.064
GPT teacher head0.358
Teacher spread0.294 · 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