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Record W4283260114 · doi:10.55034/smrv3n2-017

Jogos eletrônicos: instrumento de intervenção em idosos com a Doença do Alzheimer

2022· article· pt· W4283260114 on OpenAlexaff
Vitor Girdwood, Warley Monteiro, Edivana Almeida

Bibliographic record

VenueSTUDIES IN MULTIDISCIPLINARY REVIEW · 2022
Typearticle
Languagept
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

As intervenções cognitivas estão se tornando cada vez mais necessárias no combate da DA. Este estudo teve como objetivos identificar os efeitos dos jogos eletrônicos na intervenção na DA, descrever as principais funções cognitivas exercitadas e destacar a sua eficiência no funcionamento cognitivo dos idosos com ou sem a DA. Foi realizada uma revisão sistemática da literatura em artigos provenientes de pesquisas empíricas em bases eletrônicas de dados científicos, sendo realizada análise interpretativa. Os resultados indicaram efeitos positivos nas funções cognitivas, principalmente na atenção, memória e linguagem ao se utilizar os jogos eletrônicos, promovendo benefícios ao funcionamento cognitivo. Concluímos que há um efeito positivo do uso de jogos eletrônicos em diferentes domínios das funções cognitivas, além de parecer estimular os processos cerebrais no envelhecimento patológico e promover benefícios neuropsicológicos, demonstrando eficácia tanto para prevenção de agravos cognitivos, quanto para o tratamento ou reabilitação de idosos com diagnóstico da DA.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.278
GPT teacher head0.506
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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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