Editorial: Assessment and management in violence and aggression
Notice bibliographique
Résumé
Absolute rates of violence in mental disorders are low and people with mental illness are similarly likely to be victims as perpetrators of violence [1][2][3] . Violence associated with mental illness is however clinically relevant, as the risk is raised for people with certain mental illnesses relative to the general population [4][5][6] . Violence is an important outcome both for individuals and from a public health perspective 7 . Human costs include physical and psychological harms to victims and their families, and negative health, social and criminal justice consequences for perpetrators, such as those resulting from having a criminal conviction 8,9 . Direct economic costs to health systems result from activities such as mandatory or more intensive treatment, with indirect costs accruing to other systems, such as the criminal justice apparatus and across wider society, for example due to lost productivity 10 .While there is evidence that treating mental illness can reduce the risk of violence, it is a complex relationship with multiple causations [11][12][13][14] . This necessitates a holistic approach to assessment and management that considers individual symptoms, behaviours, interpersonal relationships and social context 15 . A better understanding of causal pathways to violence in those with mental illness would enable preventive measures to be developed and appropriately targeted 16 . Risk assessment tools could allow insights from epidemiological studies to be operationalised to assist professionals in making more informed decisions across a range of clinical and other settings 17,18 . New strategies to reduce aggression are needed and must be robustly evaluated, such as through appropriately designed clinical trials 19 .This research topic brings together five papers examining many of these elements. It spans from investigations of how early childhood experiences relate to adult aggression, to an examination of the characteristics of people receiving mandatory treatment in China.Recognising that the origin of aggression may lie in our early experiences, Koolschijn et al. studied the effect of childhood maltreatment on several outcomes, including aggression, in a sample of 128 forensic psychiatric patients 20 . These authors found that higher scores on measures of childhood maltreatment were associated with higher aggression and violence risk assessment scores. They highlight the need to consider a patient's history of maltreatment to guide risk assessment and treatment approaches. Notably, the presence and severity of childhood maltreatment is found to be a risk factor for violence and offending in the general population, as well as in clinical samples. This association has public health implications for prevention and early intervention, as initiatives to combat child maltreatment could be effective at reducing levels of aggression at a population level [21][22][23] . Schizophrenia is a condition with higher rates of violence compared to the general population, as well as to other forms of mental disorder 4 . Sagayadevan et al. explored how schizophrenia symptom severity could mediate the relationship between aggression, impulsivity and quality of life outcomes in a sample of 397 mental health outpatients with schizophrenia-spectrum disorders in Singapore. Their analysis found indirect associations between motor impulsivity and self-control with various aspects of quality of life, through symptom severity. This helps elucidate one possible pathway by which impulsivity may impact on quality of life in this population.A key step in reducing violent outcomes for clinical populations is to more readily and consistently integrate knowledge of predictors of violence into clinical practice to better individualise treatment [24][25][26] . To do this effectively we need to have robustly validated risk prediction models tailored to the populations they are used in 27 . Roaldset et al. validated a new violence risk screening tool for young people called V-RISK-Y, adapted from the well-established V-RISK-10 for adults 28,29 . They found V-RISK-Y had an Area Under the Curve of 0.762 for violent behaviour in 67 adolescents admitted to a Norwegian emergency department. V-RISK-Y was also liked by staff who used it. The authors suggest changes to V-RISK-Y and propose further research to evaluate the revised tool.An important issue relating to violence in psychiatric inpatient settings is the associated use of restrictive interventions 30 . Whilst sometimes essential for safety, the practice raises ethical issues and there is increasing emphasis on minimising its use. Hirsch et al. investigated whether implementing new guidelines reduced coercion in German psychiatric wards. The paper presented here as part of the PreVCo randomised controlled trial 31 , examines the baseline characteristics of 55 wards randomly allocated in matched pairs to the new guidelines or waiting list control 32 . Coercion rates varied widely between wards, with an association between the frequency of coercive measures used and the percentage of involuntarily admitted cases. There was no difference in the rates of coercion between wards assigned to the intervention versus controls. Fidelity of guideline implementation also varied considerably among intervention wards. The paucity of randomised evidence of this nature in forensic settings is notable, so this study is significant for overcoming the many practical and ethical barriers to such work 33 .Concerns regarding imminent risk of violence is associated with mandatory treatment under many international mental health legal frameworks 34 . Qiu et al. studied the characteristics of people subject to mandatory treatment in China under the Criminal Procedures Law of 2013 35 . They found a year-on-year increase in the number of people subject to this provision from 2013, when the law was instituted, to 2019, followed by a sharp decline in 2020 and 2021, coinciding with the Covid-19 pandemic and associated restrictions. Most applications for mandatory treatment were approved and schizophrenia was the commonest diagnosis for people subject to mandatory treatment.This collection highlights several key research priorities in violence risk assessment 36 . It is encouraging to see systematic attempts to understand the origins and patterns of violence in people with serious mental illness, as this can advance efforts to provide more tailored interventions for different types of violence and symptoms. Risk assessment tools require careful refinement for utility and efficiency, to ensure they are operating in expected ways within the specific population of interest. As compared to other fields of medicine, evidence from randomised controlled trials is lacking, and the efforts of Hirsch and colleagues are especially notable in this regard. Finally, systemslevel approaches, such as the one adopted by Qui et al., are under-utilized, but vital to place patient-and ward-level findings on the association between mental illness and violence in their appropriate context.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,023 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 0,022 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».