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Record W2339569547 · doi:10.1177/2327857915041001

Improving the Ergonomics of Cognitive Assessment with Serious Games

2015· article· en· W2339569547 on OpenAlex
Tiffany Tong, Joanna Yeung, Janahan Sandrakumar, Mark Chignell, 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 · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUsabilityCognitionCognitive ergonomicsHuman factors and ergonomicsUSableHealth carePopulationComputer scienceHeuristic evaluationHuman–computer interactionApplied psychologyPoison controlPsychologyMedicineMultimediaMedical emergency

Abstract

fetched live from OpenAlex

Current cognitive testing methods in the elderly rely on clinical assessments, which are time consuming, costly, and require highly trained staff (Kueider, Parisi, Gross, & Rebok, 2012). We are developing a serious game with the goal of improving the ergonomics of cognition assessment. Instead of pencil and paper, or a computer, we are using touch-based tablets in order to provide a highly mobile and usable form of cognitive assessment. We are currently conducting usability studies on elderly adults in different healthcare environments to evaluate the technology. This paper presents work on customizing the game for use by elderly adults in a hospital emergency department (ED) and it will discuss some of the results obtained thus far, focusing on the usability of the game for this clinical population. In addition to usability results we will also report on the validity of the game in terms of how well it agrees with existing methods of cognitive assessment that are used in the ED.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.309

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 head0.028
GPT teacher head0.335
Teacher spread0.307 · 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