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Features of Emotional Intelligence, Empathy and Alexithymia in Persons Dependent on Psychoactive Substances with Different Experiences of Their Use

2023· article· en· W4383500716 on OpenAlexaboutno aff
Y.A. Kochetova, I.A. Golovanova, M.V. Klimakova

Bibliographic record

VenueRUDN Journal of Psychology and Pedagogics · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaEmpathyPsychologyEmotional intelligenceIntrapersonal communicationClinical psychologyToronto Alexithymia ScaleSubstance abuseDevelopmental psychologyPsychiatrySocial psychologyInterpersonal communication

Abstract

fetched live from OpenAlex

The research is aimed at identifying the specifics of the components of emotional intelligence (EQ) in persons with different experiences of substance abuse. The study involved 157 respondents aged 35 to 45 years, of whom 111 were dependent on psychoactive substances and 46 never used them. The empirical study was carried out using The Emotional Intelligence Questionnaire (EmIn) by D.V. Lyusin, The Balanced Emotional Empathy Scale (BEES) by A. Mehrabian and N. Epstein, and The Toronto Alexithymia Scale (TAS). Significant differences were found for almost all the variables (except for empathy and the ‘intrapersonal management’ EQ component) between the group of persons who did not use psychoactive substances and the groups of persons dependent on such substances. The obtained results also make it possible to speak about differences in the correlations between the components of emotional intelligence with each other, as well as with empathy and alexithymia among the groups of subjects with different experiences of substance abuse. In individuals who did not use psychoactive substances, all the components of emotional intelligence are interconnected. In the addicts, as the duration of substance abuse increases, the number of connections between the components of emotional intelligence, both among themselves and with alexithymia, decreases.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.476

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.113
GPT teacher head0.397
Teacher spread0.284 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueRUDN Journal of Psychology and PedagogicsSame topicEmotional Intelligence and PerformanceFrench-language works237,207