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Record W2762886435 · doi:10.1002/jts.22226

Emotion Dysregulation and Social Support in PTSD and Depression: A Study of Trauma‐Exposed Veterans

2017· article· en· W2762886435 on OpenAlexaff
Daniel W. Cox, Anne Bakker, James A. Naifeh

Bibliographic record

VenueJournal of Traumatic Stress · 2017
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDepression (economics)PsychologySocial supportClinical psychologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Abstract Emotion dysregulation has been associated with impaired interpersonal functioning and increased risk of posttraumatic psychopathology. Given that social support is a robust predictor of psychiatric morbidity following trauma exposure, we examined whether emotion dysregulation was associated with posttraumatic psychopathology through its negative effect on social support. Using self‐report data from 90 military veterans (89.9% men) enrolled in an outpatient psychotherapy program for posttraumatic stress disorder (PTSD), we found that social support partially mediated the effect of emotion dysregulation on PTSD ( P M = .10) and depression symptoms ( P M = .14). When source of support was considered, friend ( P M = .08) and significant other support ( P M = .06) were greater mediators of the effect of emotion dysregulation on depression symptoms than family support ( P M = .01). There were no differential mediating effects for support providers on PTSD symptoms. Our findings indicate that social support is a statistically significant yet clinically limited mechanism through which emotion dysregulation is linked with psychiatric symptoms. Implications for these limitations and alternative potentially relevant interpersonal mechanisms 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)0.001
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 score0.375
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.131
GPT teacher head0.417
Teacher spread0.286 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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