Practice‐based outcomes of dialectical behaviour therapy (DBT) targeting anger and violence, with male forensic patients: a pragmatic and non‐contemporaneous comparison
Bibliographic record
Abstract
OBJECTIVE: To examine the effectiveness of an eighteen-month treatment based on dialectical behaviour therapy (DBT) targeting anger and violence, on a group of male forensic patients. METHOD: Eight male forensic patients in a high security hospital who met the criteria for borderline personality disorder measured by the Personality Assessment Inventory underwent 18 months of treatment. They completed three psychometric tests at pre-, mid- and post-treatment and at a six-month follow up. A comparison group (TAU) of nine patients, assessed as having similar personality disorders, received the usual treatment available in the hospital but excluding DBT. They completed the same tests at the same time intervals corresponding to the pre-, mid- and post-testing of the DBT group. In both groups, all instances behaviours related to anger and violence were monitored for three six-month periods, prior to, during and post-treatment. RESULTS: Overall, patients in the DBT group made greater gains than patients in the TAU group in reducing the seriousness of violence-related incidents, and in self report measures of hostility, cognitive anger, disposition to anger, outward expression of anger and anger experience. CONCLUSION: The results suggest a potential for DBT to impact positively and lastingly on violent behaviour and components of anger in male forensic patients when compared with standard treatment. The power of the current study to detect group differences was reduced by small ns, large confidence intervals, and a non-contemporaneous comparison group. Cost-effective strategies are proposed to take forward research on DBT with this population.
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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) | 0.001 | 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) | 0.003 | 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".