A preliminary evaluation of the effectiveness of dialectical behaviour therapy in a forensic psychiatric setting
Bibliographic record
Abstract
Dialectical behaviour therapy (DBT) is a therapy model incorporating elements of Eastern philosophies and cognitive behavioural principles. Originally designed for people struggling with chronic suicidality and borderline personality disorder (BPD), it has been adapted to treat complex, multi-diagnostic presentations, such as those in forensic mental health settings. To date, there has been little evaluation when the primary diagnosis is of psychosis. To explore the effectiveness of DBT, with patients, with multiple comorbidities, including psychosis, in a forensic psychiatric inpatient setting. A descriptive outcome study with a cohort of offender-patients in one specialist forensic mental health unit. Before and after treatment change scores were compared on anger, aggression, hopefulness, coping abilities, emotional intelligence, insight and subjective symptom severity scales, as well as staff-rated risk, and length of stay. Nine men and five women residents in one Canadian secure hospital completed a standard DBT programme, and self-ratings, over about 1 year. Scale scores indicated significantly increased insight and acknowledgment of problems. Apparently increased anger and vengeance scores were clinically associated. Independent staff ratings indicated reductions in risk and most patients achieved early release. This study provides support for extension of the use of DBT to offender-patients with psychosis among the complex mix in their presentation. It suggests that a randomised controlled trial with cost-benefit analysis is warranted, as well as further work, to promote understanding of mechanisms of effectiveness.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".