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Record W2165589068 · doi:10.1002/jclp.20723

Prospective evaluation of a cognitive vulnerability‐stress model for depression: the interaction of schema self‐structures and negative life events

2010· article· en· W2165589068 on OpenAlexaff
Pamela M. Seeds, David J. A. Dozois

Bibliographic record

VenueJournal of Clinical Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologySchema (genetic algorithms)Cognitive vulnerabilityCognitionVulnerability (computing)Clinical psychologyDepression (economics)Developmental psychologyDepressive symptomsPsychiatry

Abstract

fetched live from OpenAlex

This study tested the diathesis-stress component of Beck's (1967) cognitive theory of depression. Initially, participants completed measures assessing cognitive organization of the self-schema and depressive symptoms. One year later, participants completed measures assessing cognitive organization of the self-schema, depressive symptoms, and negative life events. Hierarchical multiple regression analyses, controlling for initial depression, indicated that more tightly interconnected negative content was associated with greater elevations in depressive symptoms following the occurrence of life events. More diffusely interconnected positive content for interpersonal self-referent information also interacted with life events to predict depressive symptoms. Cognitive organization dimensions showed moderate to high stability across the follow-up, suggesting that they may be trait-like vulnerability factors. Implications for the cognitive vulnerability-stress model of depression 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)0.007
metaresearch head score (Gemma)0.011
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.363
GPT teacher head0.644
Teacher spread0.280 · 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

Citations45
Published2010
Admission routes1
Has abstractyes

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