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Record W1979609266 · doi:10.5964/ejop.v10i2.671

Mediating Role of Cognitive Emotion Regulation Strategies on the Relationship Between Attachment Styles and Alexithymia

2014· article· en· W1979609266 on OpenAlexaboutno aff
Mohammad Ali Beshārat, Vahideh Shahidi

Bibliographic record

VenueEurope’s Journal of Psychology · 2014
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
FundersUniversity of Tehran
KeywordsAlexithymiaPsychologyAttachment theoryToronto Alexithymia ScaleCognitionCognitive styleAmbivalenceDevelopmental psychologyClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

The main objective of the present study was to investigate the mediating role of cognitive emotion regulation strategies on the relationship between attachment styles and alexithymia. Five hundred and thirty six undergraduate students (282 girls, 254 boys) from public universities in Tehran participated in this study. Participants were asked to complete the Adult Attachment Inventory (AAI), the Farsi version of the Toronto Alexithymia Scale (FTAS-20), and Cognitive Emotion Regulation Questionnaire (CERQ). The results illustrated a significant negative correlation between secure attachment style and alexithymia. Moreover, the results revealed a significant positive correlation between avoidant and ambivalent attachment styles with alexithymia. Regression analysis showed that both adaptive and maladaptive cognitive emotion regulation strategies, have a mediating role on the relationship between attachment styles and alexithymia. Secure and insecure attachment styles predicted changes in alexithymia through adaptive and maladaptive cognitive emotion regulation strategies in opposite directions. Based on these findings, it can be concluded that the mediating role of cognitive emotion regulation strategies on the relationship between attachment styles and alexithymia is partial.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.023
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.048
GPT teacher head0.349
Teacher spread0.301 · 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

Citations43
Published2014
Admission routes1
Has abstractyes

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