Cognitive behavior therapy in early psychosis with a focus on depression and low self-esteem: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Patients in early phases of psychosis often struggle with depressive symptoms and low self-esteem. The main aims of the present study were to examine whether cognitive behavior therapy (CBT) compared to treatment as usual (TAU) would reduce depressive symptoms (primary outcome) and increase self-esteem (secondary outcome). Furthermore, we wanted to examine whether CBT reduces symptoms measured with the PANSS (positive, negative, cognitive, or excited symptoms) or increases general functioning compared to TAU. METHODS: A total of 63 early psychosis patients were included and randomly assigned to receive either CBT (maximum 26 sessions) or TAU for a period of up to six months. A linear mixed model was used for longitudinal analysis, with a focus on whether patients in the CBT group or the TAU group changed differently to one another between the baseline and 15-month follow-up. RESULTS: There were no differences between the CBT group and TAU group regarding improvements in depressive symptoms measured with the Calgary Depression Scale for Schizophrenia (P = 0.188) or self-esteem measured with the Rosenberg Self-Esteem Scale (P = 0.580). However, patients in the CBT group improved significantly more on negative symptoms (P = 0.002) and social functioning (P = 0.001). CONCLUSIONS: We did not find CBT to be more effective than TAU in reducing depressive symptoms or increasing self-esteem in patients with early psychosis. However, CBT seems to improve negative symptoms and functioning. These results still need to be replicated in further studies as the present one was merely an exploratory analysis. ClinicalTrials.gov Identifier: NCT01511406.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".