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Record W4411492059 · doi:10.22456/2316-2171.109588

PREDIÇÃO DE HABILIDADES COGNITIVAS POR MEIO DAS CAPACIDADES FÍSICAS DE ACORDO COM SEXO

2023· article· pt· W4411492059 on OpenAlexaboutno aff
Jeniffer Ferreira-Costa, José Matos Raider, Fernanda Botta Tarallo, Mauricio Santos Martins Lopes, Johannes Carl Freiberg Neto, Julia Maria D’Andréa Greve, José Maria Montiel, Angélica Castilho Alonso

Bibliographic record

VenueEstudos Interdisciplinares sobre o Envelhecimento · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicHealth, Education, and Aging
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

Este estudo teve como objetivo analisar se as capacidades físicas e nível de escolaridade de idosos são capazes de predizer suas habilidades cognitivas, considerando ainda se há influência do sexo na execução de tarefas simultâneas. Para tanto, foram avaliados 100 idosos com 60 anos ou mais de idade, de ambos os sexos, a partir da utilização dos instrumentos Montreal Cognitive Assesment (MoCA), a fim de verificar comprometimentos cognitivos, mensuração da força de preensão palmar por meio da medida no dinamômetro Jamar®, e mensuração da mobilidade funcional pelo teste Timed Get Up and Go (TUG), com e sem tarefa cognitiva. Os resultados obtidos neste estudo demonstraram que a idade, o nível de escolaridade e as capacidades físicas, a especificar a força de preensão palmar e mobilidade funcional, são capazes de predizer o desempenho no teste cognitivo MoCA, além de que os valores preditores encontrados são diferentes conforme o sexo em idosos. Concluiu-se que há relações entre capacidades físicas e o desempenho cognitivo em idosos, sendo esta última variável influenciada pelo grau de escolaridade apresentada pelo indivíduo. Ainda, cita-se a importância de adotar dinâmicas intervencionistas eficazes e mais adaptadas conforme o sexo, a fim de proporcionar uma melhora cognitiva na população idosa.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.050
GPT teacher head0.399
Teacher spread0.349 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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