A closer look at cognitive and interpersonal variables in major depressive disorder
Bibliographic record
Abstract
AbstractMajor Depressive Disorder (MDD) causes significant distress and has major societal implications. For example, it is one of the costliest disorders in Canada (Stephens & Joubert, 2001). While, Cognitive Behavior Therapy (CBT) has proven to be effective for the treatment of depression (Butler, Chapman, Forman, & Beck, 2006; Dobson, 1989; Fava et al., 2004), few studies have examined its mechanisms of change (Castonguay, Hayes, Goldfried, & DeRubeis, 1995). Theoretically, CBT targets maladaptive cognitions, and coping strategies. Recently, the interpersonal functioning of depressed individuals has also been highlighted in CBT. One of the objectives of this thesis is to gain an understanding of the association between coping, cognitive errors and interpersonal behaviors among individuals with depression. This dissertation consists of three manuscripts. The first manuscript focused on examining the association between cognitive errors and interpersonal behaviors. The second manuscript assessed the association between coping strategies and interpersonal functioning. Lastly, the third manuscript examined whether cognitive processes serve to mediate the relationship between interpersonal functioning and depression. Results and clinical implications for each study were discussed in the context of improving psychotherapy outcome.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".