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Record W2049174658 · doi:10.1016/j.sbspro.2011.10.020

Emotional Intelligence, Alexithymia, and Interpersonal Problems

2011· article· en· W2049174658 on OpenAlexaboutno aff
Bibinaz Ghiabi, Mohammad Ali Beshārat

Bibliographic record

VenueProcedia - Social and Behavioral Sciences · 2011
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyEmotional intelligenceAssertivenessInterpersonal communicationToronto Alexithymia ScaleInterpersonal relationshipDescriptive statisticsScale (ratio)Clinical psychologyDevelopmental psychologySocial psychologyStatistics

Abstract

fetched live from OpenAlex

The aim of this study was to examine the relations of emotional intelligence with alexithymia and interpersonal problems in a sample of students. A correlational analysis was performed to assess the kind of association that exists between emotional intelligence, alexithymia and interpersonal problems. Three hundred and fifty seven students (147 boys, 210 girls) from the University of Tehran were included in this study. All participants were asked to complete Emotional Intelligence Scale (EIS), Farsi version of the Toronto Alexithymia Scale (FTAS-20), and Inventory of Interpersonal Problems (IIP). Analysis of the data involved both descriptive and inferential statistics including means, standard deviations, multivariate analysis of variance, Pearson's correlation coefficients and regression analysis. The results revealed that emotional intelligence was negatively associated with both alexithymia and different aspects of interpersonal problems including assertiveness, sociability, intimacy, and responsibility. Results of regression analysis revealed that emotional intelligence can predict alexithymia and interpersonal problems regarding assertiveness, sociability, intimacy, and responsibility. It can be concluded that emotional intelligence is associated with alexithymia and interpersonal problems. Results and implications are discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.271
GPT teacher head0.405
Teacher spread0.133 · 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 teacher head, not a consensus.

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

Citations16
Published2011
Admission routes1
Has abstractyes

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