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Record W2901339582 · doi:10.3138/jmvfh.2017-0040

Social causation or social erosion? Evaluating the association between social support and PTSD among Veterans in a transition program

2018· article· en· W2901339582 on OpenAlexaffvenue
Daniel W. Cox, Leah M. Baugh, Katherine D. McCloskey, Megumi Iyar

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2018
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCausationSocial supportPsychosocialAssociation (psychology)PsychologyClinical psychologyContext (archaeology)Social inhibitionSocial environmentPsychiatrySocial anxietySocial psychologyAnxietyPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

Introduction: Social support’s association with posttraumatic stress disorder (PTSD) in Veterans is well established. One explanation for this link is social causation – support inhibits PTSD. Inversely, within the social erosion model, PTSD erodes support. The aim of the present study was to examine if the social causation or social erosion model better explained the association between support and PTSD within a psychosocial intervention context. Methods: Veterans ( N = 218) participating in a multimodal transition program were assessed pre-program, post-program, and at 3-month follow-up on their perceived social support and PTSD symptoms. We used path analysis to conduct a three-wave cross-lagged panel model to compare the social erosion and social causation models. Results: PTSD symptoms were associated with attenuated improvements in social support, while social support was not associated with increased reductions in PTSD symptoms. This association was observed from pre- to post-program and from post-program to follow-up. Discussion: These findings support the social erosion model over the social causation model. Clinical implications of PTSD inhibiting interpersonal gains 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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.195
GPT teacher head0.490
Teacher spread0.295 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

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