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Record W4309516588 · doi:10.1111/bdi.13274

Mundo de Pólus serious game for people with bipolar disorder

2022· article· en· W4309516588 on OpenAlexaff
Adriana Inocenti Miasso, Ellen Carolina Dias Castilho, Luciana Mara Monti Fonseca, Kelly Graziani Giacchero Vedana, Cristiane von Werne Baes, Paulo Celso Prado Telles Filho, Jaime E. C. Hallak, Kathleen Hegadoren

Bibliographic record

VenueBipolar Disorders · 2022
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of Alberta
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsBipolar disorderPsychologyInterpersonal communicationPsychotherapistPsychiatryMoodSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Serious games are play-based technologies designed to teach users a wide range of concepts and skills applicable in the non-virtual environment. OBJECTIVES: This paper describes the process of developing a serious game for people with bipolar disorder to promote symptom recognition and the safe use of medications. METHODS: This study was based on the User-Centered Design methodological model and the theoretical framework for Participatory Design. We conducted interviews with health professionals and discussion circles with people with bipolar disorder and their family members in order to identify the learning needs related to symptom recognition and safe medication use. A categorical analysis was completed of the participants' reports and the scientific literature and formed the basis for the design of Mundo de Pólus. RESULTS: The game development process had three pillars (detailed in this manuscript): missions, simulation, and journal. The serious game focuses on the users' perceptions about their experience with the disorder, their interpersonal relationships, coping strategies, use of medications, and non-pharmacological treatments. CONCLUSIONS: These scientific and technological outcomes are useful to promote literacy and safety in medication therapy for people with bipolar disorder.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.235
Teacher spread0.229 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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