Could Expanding and Investing in First-Episode Psychosis Services Prevent Aggressive Behaviour and Violent Crime?
Bibliographic record
Abstract
OBJECTIVE: Some persons developing, or presenting, schizophrenia engage in aggressive behaviour (AB) and/or criminal offending. Most of these individuals display AB prior to a first episode of psychosis (FEP). In fact, approximately one-third of FEP patients have a history of AB, some additionally display other antisocial behaviours (A+AB). The large majority of these individuals have presented conduct problems since childhood, benefit from clozapine, have extensive treatment needs, and are unlikely to comply with treatment. A smaller sub-group begin to engage in AB as illness onsets. A+AB persists, often for many years in spite of treatment-as-usual, until a victim is seriously harmed. This article proposes providing multi-component treatment programs at FEP in order to prevent aggressive and antisocial behaviours of persons with schizophrenia. METHOD: Non-systematic reviews of epidemiological studies of AB among persons with schizophrenia, of the defining characteristics of sub-types of persons with schizophrenia who engage in AB and their responses to treatment, and of FEP service outcomes. RESULTS: Studies have shown that mental health services that simultaneously target schizophrenia and aggressive behaviour are most effective both in reducing psychotic symptoms and aggressive behaviour. Evidence, although not abundant, suggests that a multi-component treatment program that would include the components recommended to treat schizophrenia and cognitive-behavioural interventions to reduce A+AB, and the other factors promoting A+AB such as substance misuse, victimisation, and poor recognition of emotions in the faces of others has the potential to effectively treat schizophrenia and reduce A+AB. Patients with a recent onset of AB would require few components of treatment, while those with prior conduct disorder would require all. Such a program of treatment would be long and intense. CONCLUSIONS: Trials are needed to test the effectiveness of multi-component treatment programs targeting schizophrenia and A+AB at FEP. Studies are also necessary to determine whether providing such programs in hospitals and/or prisons, with long-term community after-care, and in some cases with court orders to participate in treatment, would enhance effectiveness. Whether investing at FEP would be cost-effective requires investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".