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Diferenças quanto à escolaridade em adultos no desempenho no teste de cancelamento dos sinos

2012· article· pt· W2065372090 on OpenAlexaff
Rochele Ferronato Correa da Silva, Caroline de Oliveira Cardoso, Róchele Paz Fonseca

Bibliographic record

VenueEstudos de Psicologia (Natal) · 2012
Typearticle
Languagept
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsQUAD Engineering (Canada)
Fundersnot available
KeywordsPsychologyGerontologyMathematics educationMedicine

Abstract

fetched live from OpenAlex

O papel de variáveis sociodemográficas na cognição humana, em especial da escolaridade, vem sendo estudado em indivíduos saudáveis. Esta pesquisa objetivou verificar se há diferenças entre grupos de diferentes níveis educacionais no processamento atencional visual avaliado pelo Teste de Cancelamento dos Sinos. Participaram 124 adultos jovens, distribuídos em três grupos, 5-8, 9-11 e 12 ou mais anos de educação formal, emparelhados por idade. As variáveis de acurácia e tempo, assim como os resultados qualitativos de estratégias utilizadas, foram comparados entre grupos (ANCOVA One-Way e Qui-quadrado). Observou-se uma discrepância entre dados quantitativos e qualitativos. Os grupos de diferentes escolaridades não se diferenciaram entre si quanto a acurácia e tempo; houve, porém, diferenças significativas quanto à distribuição de participantes por coluna em que o primeiro sino foi cancelado e pelas estratégias de cancelamento utilizadas. Novos estudos são relevantes com grupos de diferentes idades e com populações clínicas neurológicas e/ou psiquiátricas.

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)0.005
metaresearch head score (Gemma)0.021
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 score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.278
Teacher spread0.251 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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