Acceptance and commitment therapy for psychosis and trauma: Improvement in psychiatric symptoms, emotion regulation, and treatment compliance following a brief group intervention
Bibliographic record
Abstract
OBJECTIVES: Acceptance and Commitment Therapy (ACT) has shown effectiveness for individuals with psychosis and individuals with a history of childhood trauma, but has not been investigated with people with psychosis who also have a history of childhood trauma. This study aims at determining the efficacy of a mindfulness-based ACT with this clientele in diminishing psychiatric symptoms, trauma-related symptoms, as well as in improving treatment adherence. DESIGN AND METHODS: Fifty participants meeting our inclusion criteria were recruited and randomized to take part in either 10 sessions of ACT group, or Treatment as Usual (TAU). RESULTS: Using RCT it was found that symptom severity, for both overall symptoms (BPRS) and anxiety (GAD), decreased over the course of the treatment, and participants' ability to regulate their emotional reactions (i.e., accept them) increased. The study also found that treatment engagement increased with regards to help-seeking for those in the ACT group, compared with the TAU controls. CONCLUSIONS: Acceptance and Commitment Therapy offered in a group appears a promising treatment for those with psychosis and history of trauma. PRACTITIONER POINTS: To understand the benefits of ACT with those who suffer from psychosis and a history of trauma. To further the understanding of the effectiveness of ACT.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".