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Record W2115133677 · doi:10.1037/0022-006x.75.3.447

Relationship of posttreatment decentering and cognitive reactivity to relapse in major depression.

2007· article· en· W2115133677 on OpenAlexaff
David M. Fresco, Zindel V. Segal, Tom Buis

Bibliographic record

VenueJournal of Consulting and Clinical Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychologyReactivity (psychology)MoodDepression (economics)FeelingCognitionClinical psychologyDysfunctional familyCognitive therapyAntidepressantPsychotherapistPsychiatryAnxietyMedicine

Abstract

fetched live from OpenAlex

Z. V. Segal et al. (2006) demonstrated that depressed patients treated to remission through either antidepressant medication (ADM) or cognitive-behavioral therapy (CBT), but who evidenced mood-linked increases in dysfunctional thinking, showed elevated rates of relapse over 18 months. The current study sought to evaluate whether treatment response was associated with gains in decentering-the ability to observe one's thoughts and feelings as temporary, objective events in the mind-and whether these gains moderated the relationship between mood-linked cognitive reactivity and relapse of major depression. Findings revealed that CBT responders exhibited significantly greater gains in decentering compared with ADM responders. In addition, high post acute treatment levels of decentering and low cognitive reactivity were associated with the lowest rates of relapse in the 18-month follow-up period.

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)0.007
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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 integrity0.0000.001
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 head0.115
GPT teacher head0.484
Teacher spread0.369 · 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

Citations264
Published2007
Admission routes1
Has abstractyes

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