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Record W2002716241 · doi:10.1037/a0016886

Changes in self-schema structure in cognitive therapy for major depressive disorder: A randomized clinical trial.

2009· article· en· W2002716241 on OpenAlexaff
David J. A. Dozois, Peter Bieling, Irene Patelis‐Siotis, Lori Hoar, Susan Chudzik, Katie McCabe, Henny A. Westra

Bibliographic record

VenueJournal of Consulting and Clinical Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsYork UniversitySt. Joseph’s Healthcare HamiltonWestern University
FundersNational Alliance for Research on Schizophrenia and Depression
KeywordsPsychologySchema (genetic algorithms)Clinical psychologyCognitive therapyRandomized controlled trialPsychotherapistCognitionMajor depressive disorderPsychiatryMedicineInformation retrieval

Abstract

fetched live from OpenAlex

Negative cognitive structure (particularly for interpersonal content) has been shown in some research to persist past a current episode of depression and potentially to be a stable marker of vulnerability for depression (D. J. A. Dozois, 2007; D. J. A. Dozois & K. S. Dobson, 2001a). Given that cognitive therapy (CT) is highly effective for treating the acute phase of a depressive episode and that this treatment also reduces the risk of relapse and recurrence, it is possible that CT may alter these stable cognitive structures. In the current study, patients were randomly assigned to CT+ pharmacotherapy (n = 21) or to pharmacotherapy alone (n = 21). Both groups evidenced significant and similar reductions in level of depression (as measured with the Beck Depression Inventory-II and the Hamilton Rating Scale for Depression), as well as automatic thoughts and dysfunctional attitudes. However, group differences were found on cognitive organization in favor of individuals who received the combination of CT+ pharmacotherapy. The implications of these results for understanding mechanisms of change in therapy and the prophylactic nature of CT 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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.098
GPT teacher head0.519
Teacher spread0.421 · 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 designRandomized trial
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

Citations113
Published2009
Admission routes1
Has abstractyes

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