Dialectical behaviour therapy (DBT) for forensic psychiatric patients: An Italian pilot study
Bibliographic record
Abstract
BACKGROUND: Several previous randomised controlled trials of dialectical behaviour therapy (DBT) since Linehan's original have shown that it has an advantage over standard care or other psychological treatments, but focus is usually on suicide-related behaviours, and little is known about its effect with offender-patients. AIMS: To evaluate DBT with a group of offender-patients in the Italian high intensity therapeutic facilities-the Residenze per l'Esecuzione delle Misure di Sicurezza (REMS), established under the Italian Law 81/2014. METHODS: Twenty-one male forensic psychiatric in-patients with borderline personality disorder were enrolled and randomly assigned to 12 months of standard DBT together with all the usual REMS treatments (n = 10) or usual REMS treatments alone (n = 11). All participants completed the same pretreatment and posttreatment assessments, including the Barratt Impulsiveness Scale (BIS-11), Difficulties in Emotion Regulation Scale (DERS), and Toronto Alexithymia Scale 20 (TAS-20). RESULTS: Men receiving DBT showed a significantly greater reduction in motor impulsiveness, as measured by the BIS-11, and emotional regulation, as reflected by the DERS total score, than the controls. There were no significant differences between groups in alexithymia scores. CONCLUSIONS: Italy has innovative forensic psychiatric facilities with a new recovery-rehabilitation approach, but the ambitious goals behind these cannot be achieved by pharmacology alone. For the first time in clinical forensic settings in Italy, there has been limited access to DBT. This small pilot study suggests this is likely to help ameliorate traits associated with violent and antisocial behaviours, so a full-scale randomised controlled trial should follow.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".