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Record W2783400513

Avaliação da capacidade cognitiva: estudo comparado entre idosos institucionalizados e não institucionalizados

2016· article· pt· W2783400513 on OpenAlexaboutno aff
Mayra Biagini Oliveira, Eliane Caldas da Silva, Bruna Rodrigues Maziero

Bibliographic record

VenueDisciplinarum Scientia | Saúde · 2016
Typearticle
Languagept
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesGerontologyPsychologyPhilosophyMedicine
DOInot available

Abstract

fetched live from OpenAlex

O estudo versa sobre a tematica capacidade cognitiva em idosos institucionalizados e nao institucionalizados, avaliando a capacidade cognitiva dos participantes atraves de avaliacao cognitiva e com os resultados da avaliacao, realizar o comparativo entre ambos os resultados. Para a efetivacao dessa pesquisa foi realizado um estudo qualitativo com abordagem exploratoria, os dados qualitativos foram coletados atraves da Escala Montreal Cognitive Assessment (MoCA) e um questionario estruturado construido pelo pesquisador onde foram coletados alguns dados pessoais dos participantes. A MoCA e uma escala que analisa oito dominios cognitivos contemplando diversas tarefas em cada dominio, como funcao visuoespacial, nomeacao, memoria, atencao, linguagem, orientacao, abstracao e evocacao tardia. A necessidade de estimulacao cognitiva dos idosos se comprova atraves de estudos e pesquisas que avaliam a cognicao. Os resultados da pesquisa indicam que os participantes nao institucionalizados obtiveram uma maior pontuacao, se comparados aos institucionalizados que apresentaram maior declinio cognitivo. A analise dos dados coletados indica que a capacidade cognitiva nao e afetada da mesma maneira em ambos os grupos analisados.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.036
GPT teacher head0.331
Teacher spread0.295 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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