Positive imagery cognitive bias modification (CBM) and internet-based cognitive behavioural therapy (iCBT) versus control CBM and iCBT for depression: study protocol for a parallel-group randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: The current randomised controlled trial will evaluate the efficacy of an internet-delivered positive imagery cognitive bias modification (CBM) intervention for depression when compared with an active control condition and help establish the additive benefit of positive imagery CBM when delivered in combination with internet cognitive behavioural therapy for depression. METHODS AND ANALYSIS: Patients meeting diagnostic criteria for a current major depressive episode will be recruited through the research arm of a not-for-profit clinical and research unit in Australia. The minimum sample size for each group (α set at 0.05, power at 0.80) was identified as 29, but at least 10% more will be recruited to hedge against expected attrition. We will measure the impact of CBM on primary measures of depressive symptoms (Beck Depression Inventory-second edition (BDI-II), Patient Health Questionnaire (PHQ9)) and interpretive bias (ambiguous scenarios test-depression), and on a secondary measure of psychological distress (Kessler-10 (K10)) following the 1-week CBM intervention. Secondary outcome measures of psychological distress (K10), as well as disability (WHO disability assessment schedule-II), repetitive negative thinking (repetitive thinking questionnaire), and anxiety (state trait anxiety inventory-trait version) will be evaluated following completion of the 11-week combined intervention, in addition to the BDI-II and PHQ9. Intent-to-treat marginal and mixed effect models using restricted maximum likelihood estimation will be used to evaluate the primary hypotheses. Clinically significant change will be defined as high-end state functioning (a BDI-II score <14) combined with a total score reduction greater than the reliable change index score. Maintenance of gains will be assessed at 3-month follow-up. ETHICS AND DISSEMINATION: The current trial protocol has been approved by the Human Research Ethics Committee of St Vincent's Hospital and the University of New South Wales, Sydney. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12613000139774 and Clinicaltrials.gov: NCT01787513. This trial protocol is written in compliance with the Standard Protocol Items: recommendations for Interventional Trials (SPIRIT) guidelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.107 | 0.019 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".