Coping Strategies in Major Depression and Over the Course of Cognitive Therapy for Depression
Bibliographic record
Abstract
Background: Cognitive therapy (CT) aims to help patients recover from depression by teaching them to restructure their distorted thinking and to resolve practical problems in their lives. While studies have examined the role of cognitive variables in the treatment of depression, less research has focused on coping. Objectives: This study aimed to investigate the role of coping in depression, and changes in coping over the course of CT. Methods: Early and late therapy sessions were selected for 45 participants who received CT. Depression was assessed using the Beck Depression Inventory, and coping was assessed using the Coping Patterns Rating Scale. Results: Results indicated that information seeking , problem solving , and helplessness were the most prevalent coping strategies early in therapy; threat-based coping was correlated with depression; accommodation increased with therapy; and change in threat coping was correlated with change in depression. Conclusions: Findings indicate the importance of threat-based coping and of accommodation and delegation in depression and recovery from depression. Research and clinical implications 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".