Cognitive errors and coping patterns in Major Depressive Disorder and changes over the course of cognitive therapy
Bibliographic record
Abstract
Major Depressive Disorder (MDD), affects up to 16.2% of adults (Kessler et al., 2003), and is associated with immense personal suffering, and decreases in functioning and well-being (Scott & Sensky, 2003). The most well researched psychological treatment for depression is cognitive therapy (CT), developed by Beck and colleagues (Beck et al., 1979). Integral to CT is that negative early life experiences may create latent cognitive vulnerabilities in the form of core beliefs. Once activated by stressful events, these core beliefs may give rise to other forms of distorted cognitions such as dysfunctional attitudes, automatic thoughts, and cognitive errors, which reinforce depressive thinking and maintain symptoms of depression (Sacco & Beck, 1995). Similarly, coping patterns may also amplify or reduce the impacts of stress (Skinner et al., 2003). As such, CT aims to treat depression by reducing cognitive distortions and increasing the use of adaptive coping patterns (Oei & Free, 1995). Although the efficacy of CT has been well established (e.g., Dobson, 1989; Driessen & Hollon, 2010; Lynch, Laws, & McKenna, 2010), little is known about the mechanisms through which its successful results are achieved (Kazdin, 2007). Few studies have examined the frequency and type of cognitive errors and coping patterns in depression, nor how these variables change over the course of CT. In a series of three studies, this dissertation examined: 1. An early therapy profile of cognitive errors in depression, 2. Changes in cognitive errors from early to late cognitive therapy, 3. An early therapy profile of coping patterns in depression, and 4. Changes in coping patterns over the course of CT. Implications for research and practice are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".