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Record W2128373751 · doi:10.1080/10615800802638279

Emotion regulation strategies as mediators of the association between level of attachment security and PTSD symptoms following trauma in adulthood

2009· article· en· W2128373751 on OpenAlexafffund
Maryse Benoît, Donald Bouthillier, Ellen Moss, Cécile Rousseau, Alain Brunet

Bibliographic record

VenueAnxiety Stress & Coping · 2009
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsMcGill UniversityUniversité du Québec à MontréalHôpital du Sacré-Cœur de MontréalUniversité de Sherbrooke
FundersUniversité du Québec à Montréal
KeywordsAssociation (psychology)PsychologyInsecure attachmentClinical psychologyPosttraumatic stressAttachment theoryPsychotherapist

Abstract

fetched live from OpenAlex

Although, a link between attachment and posttraumatic stress disorder (PTSD) symptoms has been established, the mechanisms involved in this link have not yet been identified. Furthermore, attachment has been systematically measured by self-report questionnaires, which are prone to perceptual bias. The first goal of this study was to examine the link between PTSD symptoms and attachment security level, as measured with a security index created from the Adult Attachment Projective interview. The second goal was to test emotion regulation strategies as mediators of this link. Participants were recruited in hospital emergency rooms following trauma exposure in adulthood. The results showed that a higher level of attachment security was associated with fewer PTSD symptoms at one and three months post-trauma. The results also showed that substance use and emotion-focused strategies mediated the association between attachment and PTSD symptoms. Theoretical and clinical considerations that follow from these outcomes 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.356
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), 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

Citations109
Published2009
Admission routes2
Has abstractyes

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