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Record W4385621733 · doi:10.33448/rsd-v12i7.42708

Desempenho cognitivo de idosos com perda auditiva

2023· article· pt· W4385621733 on OpenAlexaboutno aff
Everton Adriano de Morais, Israel Bispo dos Santos, Carolina Lamonica Batista Marques, Amer Cavalheiro Hamdan, Ana Cristina Guarinello

Bibliographic record

VenueResearch Society and Development · 2023
Typearticle
Languagept
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsHearing lossPsychologyAudiologyMedicine

Abstract

fetched live from OpenAlex

A literatura internacional, nos últimos anos, vem demonstrando que existe uma relação entre as perdas auditivas e o desempenho cognitivo de idosos. Com o objetivo de analisar o desempenho cognitivo de idosos com perda auditiva, usuários ou não de Aparelho de Amplificação Sonora Individual (AASI) a presente pesquisa obteve oitenta participantes, idosos diagnosticados com perda auditiva e problemas socioeconômicos. Esse é um estudo quantitativo, transversal e observacional, com aplicação dos instrumentos: 1) questionário de caracterização sociodemográfica; 2) Montreal Cognitive Assessment. A análise dos dados utilizou-se de estatística descritiva e, para comparação de médias, o t de Student; para correlação das variáveis, o teste de Coeficiente de Correlação de Pearson e, para comparação das proporções das variáveis, o teste Qui-quadrado de Pearson. Os resultados evidenciaram que idosos com perda auditiva, usuários de AASI e com maior nível de instrução tiveram melhor desempenho no teste cognitivo. Os achados da pesquisa podem auxiliar profissionais da saúde no acompanhamento de idosos com perda auditiva.

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)0.004
metaresearch head score (Gemma)0.015
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
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 head0.186
GPT teacher head0.419
Teacher spread0.232 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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