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Record W4389732150 · doi:10.1080/15299732.2023.2293777

Interpersonal Trauma and Substance Use Severity: The Serial Mediation of Emotional Intolerance and Emotional Dysregulation

2023· article· en· W4389732150 on OpenAlexaff
Catherine E. Gallagher, Caroline Brunelle

Bibliographic record

VenueJournal of Trauma & Dissociation · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of New Brunswick
FundersSociale en Geesteswetenschappen, NWO
KeywordsPsychologyClinical psychologyInterpersonal communicationEmotional dysregulationDistressPopulationAnxietyMediationPsychiatryMedicineSocial psychology

Abstract

fetched live from OpenAlex

Substance use is highly prevalent in those with trauma histories, especially in women, which may be in part explained by high rates of interpersonal trauma in this population. Research examining the potential mechanisms underlying the relationship between co-occurring interpersonal trauma histories and substance use disorders (SUDs) is in its infancy. The current study examined whether the relationship between interpersonal trauma and SUD severity could be understood via the sequential ordering of two transdiagnostic emotional vulnerability factors: 1) emotional intolerance (anxiety sensitivity, distress intolerance), and 2) emotional dysregulation (negative urgency, lack of clarity, nonacceptance, limited strategies, difficulties with goal-directed behavior). A sample of 130 adult community-based women self-identifying as experiencing substance use problems completed the online survey. Mediation analyses suggest that as women's lifetime interpersonal trauma increases, so does their SUD severity by way of emotional intolerance and subsequent difficulties regulating their emotions. The findings suggests that transdiagnostic interventions targeting tolerance of aversive emotions may facilitate the ability to learn and employ healthy emotion regulation strategies among women with interpersonal trauma histories and SUDs.

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)0.000
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 score0.355
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.279
Teacher spread0.253 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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