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UI Design for Mobile Technology in a Closed Environment

2008· book-chapter· en· W2226310840 on OpenAlexaff
Kater Oakley, Gitte Lindgaard, Peter G. Kroeger, John R. Miller, Earl Bryenton, Paul M. Hebert

Bibliographic record

VenueIGI Global eBooks · 2008
Typebook-chapter
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsCanadian Medical AssociationCarleton University
Fundersnot available
KeywordsALARMMobile devicePermissionComputer scienceHuman–computer interactionPerceptionUnit (ring theory)MultimediaEngineeringWorld Wide WebPsychology

Abstract

fetched live from OpenAlex

This chapter reports on a case study linking several technology devices that monitor a range of vital signs in patients recently discharged to a hospital ward from the Intensive Care Unit (ICU). Apart from presenting an interesting technological challenge, this closed environment creates unique logistical and physical ergonomic challenges as well as cognitive and perceptual design problems for mobile technology. Devices include desktop computers, touch monitors, and several types of remote mobile devices including PDAs. A number of important design issues are addressed, such as deciding which visual details can be safely eliminated from a small display, or if permission should be given to turn off the alarm functions, among others. Lack of direct access to users compromised the ecological validity of several parts of the evaluation and alternative evaluation methods had to be devised.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
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 head0.045
GPT teacher head0.293
Teacher spread0.247 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2008
Admission routes1
Has abstractyes

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