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Record W1546225254

Alexitimia e inteligência emocional: estudo correlacional

2010· article· pt· W1546225254 on OpenAlexaboutno aff
Fabiano Koich Miguel, José Maurício Haas Bueno, Ana Paula Porto Noronha, Gleiber Couto, Ricardo Primi, Monalisa Muniz

Bibliographic record

VenueAmericanae (AECID Library) · 2010
Typearticle
Languagept
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyEmotional intelligencePerceptionThe Emotional Intelligence AppraisalToronto Alexithymia ScaleTest (biology)Scale (ratio)Social psychologyConstructiveCognitive psychologyProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

Inteligência emocional refere-se à capacidade de utilizar informação emocional para guiar pensamento e ações de maneira adaptativa e construtiva. Uma das subáreas da inteligência emocional é percepção, ou seja, a capacidade de identificar corretamente emoções em si e nos outros. O indivíduo alexitímico possui dificuldade em caracterizar o próprio estado emocional. O presente estudo teve como objetivo buscar evidências de validade para o Teste Informatizado de Percepção de Emoções em Fotos (TPE) relacionando-o com a Escala de Alexitimia de Toronto (TAS). Participaram da pesquisa 54 sujeitos que responderam aos instrumentos. O TPE foi pontuado por dois métodos: consenso da amostra e por meio da Teoria de Resposta ao Item, baseado em consenso de seis especialistas. Entre os resultados, encontrou-se que menor capacidade de fantasiar (uma área da alexitimia) está associada a menor capacidade de perceber emoções em si mesmo. Os resultados referentes à validade do instrumento são discutidos no texto. Palavras-chave: inteligência emocional; alexitimia; percepção emocional; teoria de resposta ao item; avaliação psicólogica

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1330.024

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 head0.019
GPT teacher head0.300
Teacher spread0.281 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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