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Record W3212205708 · doi:10.1002/jclp.23279

Alexithymia disrupts emotion regulation processes and is associated with greater negative affect and alcohol problems

2021· article· en· W3212205708 on OpenAlexaboutno aff
Braden K. Linn, Junru Zhao, Clara M. Bradizza, Joseph F. Lucke, Melanie Ruszczyk, Paul R. Stasiewicz

Bibliographic record

VenueJournal of Clinical Psychology · 2021
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsAlexithymiaPsychologyAffect (linguistics)Affect regulationEmotional regulationClinical psychologyAlcoholDevelopmental psychologyCommunicationChemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: Alexithymia is common among people who abuse alcohol, yet the mechanisms by which alexithymia exerts its influence remain unclear. This analysis tested a model whereby the three subscales of the Toronto Alexithymia Scale exert an indirect effect on alcohol problems through difficulties with emotion regulation and psychological distress. METHOD: Men and women (n = 141) seeking alcohol use disorder (AUD) treatment completed the Toronto Alexithymia Scale, the Difficulties with Emotion Regulation Scale, the Brief Symptom Inventory, the Short Inventory of Problems, and the Alcohol Dependence Scale. RESULTS: The Difficulty Identifying Feelings subscale of the Toronto Alexithymia Scale was positively associated with alcohol problems through emotion dysregulation and psychological distress. The other two subscales, Difficulty Describing Feelings and Externally oriented Thinking, were not associated with any other variables. CONCLUSION: People with alexithymia may consume alcohol to help regulate undifferentiated states of emotional arousal. Given the prevalence of alexithymia among people who abuse alcohol, treatment supplements that enhance the identification of emotions are needed.

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)0.001
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 score0.010
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.107
GPT teacher head0.434
Teacher spread0.327 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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