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Record W2156956782 · doi:10.1109/igic.2011.6115113

Power defense: A video game for improving diabetes numeracy

2011· article· en· W2156956782 on OpenAlexafffund
Ereny Bassilious, Aaron DeChamplain, Ian McCabe, Matthew Stephan, Bill Kapralos, Farid H. Mahmud, Adam Dubrowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsSickKids FoundationOntario Tech UniversityHospital for Sick Children
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNumeracyExperiential learningComputer scienceVideo gamePower (physics)Diabetes mellitusGame based learningHuman–computer interactionMultimediaPsychologyMathematics educationMedicineLiteracyPedagogy

Abstract

fetched live from OpenAlex

Adolescents with T1D often have poor control of their disease. With the knowledge that the current generation appreciates and learns more from interactive approaches to teaching, we have developed Power Defense, a highly interactive video game aimed at improving one particular skill associated with managing diabetes - numeracy. Diabetes-related numeracy encompasses the ability to understand and interpret results and then appropriately apply the results to the management of diabetes. Power Defense employs the principals of experiential learning and includes both implicit and explicit methods for teaching the player the necessary diabetes numeracy skills.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.994

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)0.0070.001

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 head0.033
GPT teacher head0.308
Teacher spread0.274 · 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
GenreEmpirical

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

Citations14
Published2011
Admission routes2
Has abstractyes

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