What do we know now about evidence-based treatment for psychosis and aggressive behaviour or criminality that we did not know when community care was implemented?
Bibliographic record
Abstract
PURPOSE: Community care replaced institutional care for people with psychosis without guidance about what constituted effective treatment. In a Swedish birth cohort, many of those who developed schizophrenia or bipolar disorder as community care was being implemented were subsequently convicted of violent and non-violent crimes. Studies from other countries that were implementing community care at this time also reported elevated proportions of patients acquiring criminal convictions. Since community care was first implemented, much has been learned about factors that promote and treatments that limit aggressive/antisocial behaviour/criminality (AABC) among people with psychosis. Without the benefit of this knowledge, did mental health policy and practices that were in place as the asylums were closed inadvertently contribute to criminality? MATERIAL AND METHODS: This article provides a narrative review of current evidence of effective treatments and management strategies to reduce AABC among patients with psychosis. RESULTS: Reductions in AABC are associated with stable contact with psychiatric services, second-generation antipsychotic medication, clozapine for patients with schizophrenia and elevated levels of hostility and/or a history of childhood conduct disorder, abstinence from substances, avoidance of trauma, and constant monitoring of both illness symptoms and AABC. CONCLUSIONS: Failure to adopt evidence-based practices allows the problem of AABC to persist, prevents patients from experiencing independent, safe, community tenure, and puts those around them at risk. Many challenges remain, including implementing effective assessment and interventions at first-episode and convincing patients with antisocial attitudes and behaviours to participate in treatment programs to reduce AABC and to learn prosocial behaviours.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".