Cognitive behavioural therapy for anger and aggression in forensic mental health populations
Bibliographic record
Abstract
There is a paucity of research surrounding the needs of those within forensic mental health populations. This gap is amplified when we examine CBT-based treatments for those with anger and aggression in this population. Before we can address this gap in the literature, we must survey what has been done, who it has been applied to, and how effective it has been. Thus, we aimed to conduct a review on CBT for anger and aggression among forensic mental health populations. Authors followed a preregistered Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. Databases were searched for articles April 2020 and October 2022. The searches resulted in 4,137 articles which were reduced to 22 after applying inclusion and exclusion criteria. Findings demonstrate that CBT treatments for anger and aggression, including the reasoning and rehabilitation program, dialectic behaviour therapy, and aggression control programs tend to produce consistent and favourable results within forensic mental health populations. Findings confirm the success of CBT-based treatments for anger and aggression within this subgroup of justice-involved individuals. The review expands on this notion by identifying which treatments and criteria appear to be most promising for individuals in the forensic mental health population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".