Acceptance and Commitment Therapy for Psychosis and Trauma: Investigating Links between Trauma Severity, Attachment and Outcome
Bibliographic record
Abstract
BACKGROUND: Although Acceptance and Commitment Therapy (ACT) may be effective for individuals with psychosis and a history of childhood trauma, little is known about predictors of treatment response among such patients. AIMS: The current study examined: (1) whether severity of trauma predicted treatment response, and (2) profiles of patients with regard to their responses to treatment. METHOD: Fifty participants with psychosis and childhood trauma history were recruited and randomized to take part in either eight sessions of group-based ACT, or to be on a waiting list for the ACT group (i.e. treatment as usual group). The entire sample was used for the first part of the analyses (aim 1), whereas subsequent subsample analyses used only the treatment group (n = 30 for aim 2). RESULTS: It was found that trauma severity did not moderate the effectiveness of ACT on symptom severity, participants' ability to regulate their emotional reactions, or treatment compliance with regard to help-seeking. In addition, among those receiving ACT, the results revealed three distinct and clinically relevant change profiles. Avoidant attachment style and number of sessions attended were predictive of belonging to the different clusters or profiles. Patients in the profile representing the least amount of clinical change attended an average of two sessions less than those in the other change profiles. CONCLUSION: ACT offered in a group format appears to be a promising treatment for those with psychosis and history of trauma regardless of trauma severity. Given the brevity of the intervention, patients should be encouraged to attend each session to obtain maximum benefit.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".