[New challenge for services for people with a first episode of psychosis: To integrate interventions to prevent and reduce physical aggression].
Bibliographic record
Abstract
Objective Mental health services for persons presenting a first episode of psychosis include specialized interventions that are effective in treating psychosis, but they do not include treatments that prevent aggressive behaviour (AB). This article presents the results of studies in an effort to incite these services to evaluate the risk of AB and to intervene rapidly to prevent these behaviours as well as treating the psychotic disorder. Method This article presents a narrative review of the scientific studies. We have reviewed publications in French and English reporting studies, reviews, and meta-analyses focused on the antecedents, correlates, and effective treatments for AB and criminality of persons described as presenting severe mental disorder, schizophrenia, or a first episode of psychosis. Results The review of scientific studies confirms that persons developing or presenting schizophrenia are at increased risk to engage in AB, crime, and homicide relative to the general population. Before a first episode of psychosis approximately one third of patients display AB. One subgroup of these persons, those who present the highest risk of AB and criminality, have a history of conduct disorder since childhood. Another subgroup who also begin engaging in AB before a first episode of psychosis display AB as anxiety and positive symptoms increase. The association between positive psychotic symptoms and AB varies according to the phase of illness and the age of onset of AB. Further, other factors such as substance misuse (especially cannabis use), difficulty in recognizing emotions in the faces of others, impulsivity, and physical victimization are related to AB. In addition to effective treatments for psychosis and social skill training, treatment programs aimed at preventing AB and antisocial behaviours, and the associated factors are needed, as well as interventions that aim to reduce victimization. Conclusion The extant literature identifies different profiles of patients and factors associated with AB that can be identified at a first episode of psychosis. Consequently, first episode services could play an important role in preventing AB. To do this, they would need to simultaneously treat the psychosis and the AB. The implementation of such treatments would involve challenges such as convincing treatment teams of the benefits of such an approach and convincing patients needing these treatments to comply. Increasing patients' motivation and compliance remains an important challenge.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".