Self‐critical perfectionism, dependency and entropy during cognitive behavioural therapy for depression
Bibliographic record
Abstract
OBJECTIVES: This study examined whether 'personality vulnerability' (i.e., self-critical perfectionism or dependency) predicts the trajectory of change, as well as variability and instability (i.e., entropy) of symptoms, during cognitive behaviour therapy (CBT) for depression. DESIGN: Study participants were outpatients (N = 312) experiencing a primary mood disorder. Participants received CBT for depression group sessions over 15 weeks. Self-report measures of self-critical perfectionism, dependency, and depression were collected longitudinally. METHODS: A latent growth mixture modelling (LGMM) statistical approach was used to evaluate the presence of latent classes of individuals based on their longitudinal pattern of symptom change during CBT and to evaluate whether baseline self-critical perfectionism or dependency predicts class membership. A Latent Acceleration Score (LAS) model evaluated whether perfectionism or dependency led to variability in depression symptom change (e.g., velocity) by considering changes in velocity (e.g., acceleration and/or deceleration). RESULTS: LGMM indicated the presence of two latent classes that represent symptom improvement (N = 239) or minimal symptom improvement over time (N = 73). Elevated baseline self-critical perfectionism, but not dependency, predicted a greater likelihood of membership in the class of participants who demonstrated minimal symptom improvement over time. The second analysis examined whether baseline self-critical perfectionism also predicts depression symptom variability and instability. The LAS perfectionism model demonstrated that perfectionism accelerates depression symptom change during the first seven sessions of treatment, then has a decelerating effect on depression symptom change. CONCLUSIONS: Results indicated that higher baseline self-critical perfectionism predicted higher variability and instability in depression symptoms and variability in acceleration and deceleration, over the course of treatment.
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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 | 0.006 |
| 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 | 0.001 |
| 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".