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Record W2172111387 · doi:10.1177/2327857914031004

Designing Serious Games for Cognitive Assessment of the Elderly

2014· article· en· W2172111387 on OpenAlex
Tiffany Tong, Mark Chignell, Phil Lam, Mary C. Tierney, Jacques Lee

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

VenueProceedings of the International Symposium on Human Factors and Ergonomics in Health Care · 2014
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsCognitionDeliriumUsabilityCognitive ergonomicsPsychologyApplied psychologyComputer scienceCognitive psychologyMedicineHuman–computer interactionPsychiatryMedical emergencyHuman factors and ergonomicsPoison control

Abstract

fetched live from OpenAlex

The under-diagnosis of cognitive impairments can lead to increased economic burden, hospitalization, and even death (Inouye, Bogardus, Baker, Leo-Summers, & Cooney, 2000). Many of the current cognitive tests have been developed to diagnose specific conditions. However, there is a lack of cognitive tools to assess transitory conditions that occur between normal cognition and cognitive failure such as delirium. In this paper, we discuss the development of a serious game for cognitive assessment of the elderly that can address this gap. We introduce the whack-a-mole game that we have developed and present initial findings concerning its usability and validity in university and elderly populations. We conclude by discussing the role of human factors engineering, and associated design methodologies, in developing serious games of this type.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.023
GPT teacher head0.325
Teacher spread0.302 · 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