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Record W2902329860 · doi:10.1590/2317-6431-2017-1935

Tradução e adaptação de um software de treinamento da escuta no ruído para o português brasileiro

2018· article· pt· W2902329860 on OpenAlexaff
Karenina Santos Calarga, Caroline Nunes Rocha-Muniz, Benoı̂t Jutras, Eliane Schochat

Bibliographic record

VenueAudiology - Communication Research · 2018
Typearticle
Languagept
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversité de Montréal
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

RESUMO Objetivo Traduzir e adaptar o software Logiciel d’Écoute dans le Bruit - LEB, verificar sua efetividade e jogabilidade em um grupo de escolares sem queixas auditivas e/ou de aprendizagem. Métodos A efetividade foi investigada por meio da análise do desempenho de dois grupos pareados, antes e depois do treinamento, no teste de fala comprimida. O grupo treinado (GT), constituído por 22 escolares, entre 9 a 10 anos de idade, recebeu treinamento com o software e o grupo controle (GC), composto por 20 escolares da mesma faixa etária, não recebeu nenhum tipo de estimulação. Após o treinamento, os sujeitos do GT responderam a uma avaliação qualitativa sobre o software. Resultados Os comandos foram compreendidos e executados com facilidade e eficácia. O questionário revelou que o LEB foi bem aceito e estimulante, proporcionando novos aprendizados. O GT apresentou evoluções significativas, em comparação ao GC. Conclusão O êxito na tradução, adaptação e jogabilidade do software fica evidenciado pelas mudanças observadas na habilidade de fechamento auditivo, sugerindo sua efetividade para treinamento da percepção da fala no ruído.

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)0.008
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.279
GPT teacher head0.466
Teacher spread0.186 · 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 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
Published2018
Admission routes1
Has abstractyes

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