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Record W3088479562 · doi:10.1177/1937586720959074

A User-Centered Approach to Evaluating Wayfinding Systems in Healthcare

2020· article· en· W3088479562 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

VenueHERD Health Environments Research & Design Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsSignageUsabilityComputer scienceProcess (computing)Health careHuman–computer interaction

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this methodology is to provide the designers of wayfinding systems in healthcare facilities a process for evaluating and optimizing a design prior to implementation. The use of this methodology can improve patient experience in hospitals by preventing the installation of confusing, incomplete, and/or frustrating wayfinding systems. BACKGROUND: Despite known wayfinding and information design principles, wayfinding continues to be a challenge in healthcare environments. One reason is that the design of wayfinding systems is rarely evaluated with end users prior to implementation. The methodology outlined in this article is an application of usability testing that involves end users navigating a space using prototyped signage and other elements of a wayfinding system to determine the effectiveness of the system and identify improvement opportunities. This methodology was developed for use in an emergency department that had outdated signage and required a new wayfinding system. CONCLUSION: This methodology provides a structured process for testing and improving the design of a hospital wayfinding system prior to implementation.

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)0.005
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.376
GPT teacher head0.429
Teacher spread0.053 · 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