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Record W2046371322 · doi:10.1037/a0033247

Combining imagination and reason in the treatment of depression: A randomized controlled trial of internet-based cognitive-bias modification and internet-CBT for depression.

2013· article· en· W2046371322 on OpenAlexfundno aff
Alishia D. Williams, Simon E. Blackwell, Anna Mackenzie, Emily A. Holmes, Gavin Andrews

Bibliographic record

VenueJournal of Consulting and Clinical Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilWellcome TrustUniversity of New South WalesNational Institute for Health and Care ResearchUniversity of OxfordLupina Foundation
KeywordsPsychologyThe InternetDepression (economics)Randomized controlled trialPsychotherapistCognitionClinical psychologyCognitive restructuringCognitive therapyCognitive behavioral therapyPsychiatryWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Computerized cognitive-bias modification (CBM) protocols are rapidly evolving in experimental medicine yet might best be combined with Internet-based cognitive behavioral therapy (iCBT). No research to date has evaluated the combined approach in depression. The current randomized controlled trial aimed to evaluate both the independent effects of a CBM protocol targeting imagery and interpretation bias (CBM-I) and the combined effects of CBM-I followed by iCBT. METHOD: Patients diagnosed with a major depressive episode were randomized to an 11-week intervention (1 week/CBM-I + 10 weeks/iCBT; n = 38) that was delivered via the Internet with no face-to-face patient contact or to a wait-list control (WLC; n = 31). RESULTS: Intent-to-treat marginal models using restricted maximum likelihood estimation demonstrated significant reductions in primary measures of depressive symptoms and distress corresponding to medium-large effect sizes (Cohen's d = 0.62-2.40) following CBM-I and the combined (CBM-I + iCBT) intervention. Analyses demonstrated that the change in interpretation bias at least partially mediated the reduction in depression symptoms following CBM-I. Treatment superiority over the WLC was also evident on all outcome measures at both time points (Hedges gs = .59-.98). Significant reductions were also observed following the combined intervention on secondary measures associated with depression: disability, anxiety, and repetitive negative thinking (Cohen's d = 1.51-2.23). Twenty-seven percent of patients evidenced clinically significant change following CBM-I, and this proportion increased to 65% following the combined intervention. CONCLUSIONS: The current study provides encouraging results of the integration of Internet-based technologies into an efficacious and acceptable form of treatment delivery. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.136
GPT teacher head0.500
Teacher spread0.364 · 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 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

Citations204
Published2013
Admission routes1
Has abstractyes

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