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Record W2587624376 · doi:10.1037/cfp0000050

A preliminary examination of the effects of pretreatment relationship satisfaction on treatment outcomes in cognitive-behavioral conjoint therapy for PTSD.

2015· article· en· W2587624376 on OpenAlexaff
Philippe Shnaider, Nicole D. Pukay‐Martin, Shankari Sharma, Tiffany Jenzer, Steffany J. Fredman, Alexandra Macdonald, Candice M. Monson

Bibliographic record

VenueCouple and Family Psychology Research and Practice · 2015
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsToronto Metropolitan University
FundersNational Institute of Mental Health
KeywordsPsychologyClinical psychologyPsychotherapistCognitionCognitive behavioral therapyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The primary goal of the present study was to investigate whether pre-treatment relationship satisfaction predicted treatment drop-out and posttraumatic stress disorder (PTSD) symptom outcomes within a trial of cognitive-behavioral conjoint therapy (CBCT) for PTSD (Monson & Fredman, 2012). Additionally, we examined the influence of pre-treatment relationship distress on relationship outcomes. METHOD: Thirty-seven patients and their intimate partners who participated in a course of CBCT for PTSD were assessed for PTSD symptoms with the Clinician-Administered PTSD Scale and PTSD Checklist, and for intimate relationship functioning with the Dyadic Adjustment Scale. CBCT for PTSD is a conjoint therapy designed to improve PTSD symptoms and enhance relationship functioning. Patients had to meet diagnostic criteria for PTSD to be included in the study; however, couples were not required to be in distressed relationships to receive treatment. RESULTS: = 1.02). CONCLUSIONS: Among patients receiving CBCT for PTSD, treatment drop-out and improvements in PTSD symptoms may be independent of pre-treatment relationship functioning, whereas improvements in relational functioning may be greater among those distressed prior to treatment.

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)0.001
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 score0.043
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.321
GPT teacher head0.557
Teacher spread0.236 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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