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PROTOTIPAGEM DO APLICATIVO MÓVEL MEMO COM UTILIZAÇÃO DE HEURÍSTICAS DE INTERFACE PARA PESSOA IDOSA

2025· article· W4415814676 on OpenAlexaff
Dayane Aparecida Scaramal, Sylvie Belleville, Bobby Gilbert, Isabelle Patriciá Freitas Soares Chariglione, Maria do Carmo Fernandez Lourenço Haddad, André Estevam Jaques, Rosângela Aparecida Pimenta Ferrari, Mara Solange Gomes Dellaroza

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2025
Typearticle
Language
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsClinique Neuro-OutaouaisInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsInterface (matter)User interfaceInterface designProcess (computing)

Abstract

fetched live from OpenAlex

RESUMO Objetivo: construir o protótipo de aplicativo móvel do treino cognitivo canadense Méthode d’Entrainement pour une Mémoire Optimale, com interface e design específicos para pessoas idosas. Método: estudo metodológico de produção tecnológica, incorporando heurísticas de interface de pequenas telas sensíveis ao toque para pessoas idosas, combinadas ao Paradigma da Prototipação, sistematizado em: (1) comunicação; (2) projeto rápido e modelagem; (3) construção do protótipo, e (4) emprego, entrega e realimentação. O processo de prototipagem aconteceu entre fevereiro de 2022 e agosto de 2023. Resultados: o protótipo incluiu 216 telas, divididas em seis sessões, cada uma contendo atividades de treinamento cognitivo e informações sobre envelhecimento e cognição. Um mapa conceitual foi criado, abrangendo todos os conteúdos, seguidos pela elaboração de wireframe das telas, permitindo a aplicação das heurísticas de interface, incluindo aspectos cognitivos, visuais, entrada de dados, toque e geracionais. Conclusão: a abordagem adotada, aliada às heurísticas de interface para a população idosa, resultou na criação de um protótipo exclusivo, cujos aspectos foram meticulosamente planejados e refinados para garantir uma experiência de usuário fluida e sem obstáculos, contribuindo para reduzir o gap de acessibilidade de recursos tecnológicos para estes indivíduos.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0020.004
Scholarly communication0.0010.001
Open science0.0050.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.301
GPT teacher head0.492
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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