Predictors of Depressive Relapse During a Two Year Prospective Follow-up after Cognitive and Behavioral Therapies
Bibliographic record
Abstract
Patient characteristics may partly explain individual differences in long-term maintenance of treatment gains in response to cognitive and behavioral therapies (CBT). This study evaluated the relative contribution of negative cognitive style, behavioral activation, and personality patterns, measured at the end of treatment among treatment responders, towards the risk of relapse of major depressive disorder over a 2-year prospective follow-up. The sample included depressed adults who were randomized and received an adequate dose of one of three forms of CBT, who then fully responded and completed a 2-year prospective follow-up (N = 93) (Jacobson et al., 1996; Gortner, Gollan, Dobson and Jacobson, 1998). Cox regression analyses revealed that for treatment responders, self-devaluative depressogenic cognitive style did not predict relapse during the 2-year follow-up. Separate cox regression analyses indicated that without considering the contribution of personality patterns, it was important to distinguish the level of behavioral activation (e.g. frequency of pleasant events) from its associated pleasure in predicting relapse. With the introduction of personality patterns; however, an integrated model revealed that only dependent personality pattern was associated with hazard of relapse during the 2-year prospective follow-up. Dependent personality pattern may increase relapse through behavioral passivity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".