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Record W2934368691

Fydlyty: A Low-Fidelity Serious Game for Medical-Based Cultural Competence Education

2015· article· en· W2934368691 on OpenAlexaff
Zain Khan, Kristopher Maddeaux, Bill Kapralos

Bibliographic record

VenueEAI Endorsed Transactions on Future Intelligent Educational Environments · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAvatarConversationCompetence (human resources)Serious gamePsychologyModalitiesFidelityCultural competenceComputer scienceMultimediaHuman–computer interactionSocial psychologyPedagogyCommunicationSociology
DOInot available

Abstract

fetched live from OpenAlex

Here we present Fydlyty, a web-based, low-fidelity serious game to educate, and inform medical practitioners and trainees about cultural competence. Fydlyty includes a dialogue editor which has the ability to build a conversation, interpret responses, and respond to questions/answers from the game player. These responses are based on predefined cultural characteristics of the virtual character (avatar), and on different moods that the avatar may express depending on the situation (i.e., normal, upset, or angry). In addition to its educational purposes, Fydlyty has been developed as a research tool to examine the role of graphical-based fidelity in the learning process.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.353
Teacher spread0.322 · 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 designNot applicable
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

Citations3
Published2015
Admission routes1
Has abstractyes

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