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Enregistrement W1907696037 · doi:10.1111/j.1360-0443.2006.01553.x

The relationship between homicide and alcohol, drugs and psychiatric disorder: some directions for prevention

2006· letter· en· W1907696037 sur OpenAlexaff
Kathryn Graham

Notice bibliographique

RevueAddiction · 2006
Typeletter
Langueen
DomainePsychology
ThématiquePsychopathy, Forensic Psychiatry, Sexual Offending
Établissements canadiensCentre for Addiction and Mental HealthWestern University
Organismes subventionnairesnon disponible
Mots-clésPsychiatryHomicideAddictionAlcohol dependencePsychologyInjury preventionAlcohol use disorderPoison controlAlcoholHuman factors and ergonomicsMedicineClinical psychologyMedical emergency

Résumé

récupéré en direct d'OpenAlex

A paper published by Shaw et al. [1] in this issue of Addiction is based on an unusual and impressive data set that includes psychiatric reports for almost three-quarters of homicide perpetrators over a 3-year period. From these reports, the investigators extracted information on clinical history of the perpetrator (alcohol and drug dependence or misuse, psychiatric disorder) and mental state at the time of the offense, including the extent that alcohol or drugs were judged likely to have influenced the homicide. While the data are potentially very useful, I find the results as presented difficult to interpret because the groups compared in Table 1 of their report are not mutually exclusive, including substantial overlap of the alcohol and drug misuse groups. The second problem is that the total sample comparison group appears to include the 25% who were not given a psychiatric evaluation; yet statistics for variables such as psychiatric disorders seem to have used the total sample as the denominator rather than the number who had a psychiatric evaluation. The findings would have been easier to interpret if the comparisons had been made for the following mutually exclusive categories: (1) alcohol dependence/misuse only; (2) drug dependence/misuse only; (3) dependence/misuse of both alcohol and drugs; (4) psychiatric report with no history of alcohol/drug dependence/misuse; and (5) no psychiatric report (including only variables that were available without a psychiatric report). Despite these limitations, however, some patterns emerged related to alcohol and drugs. Consistent with other research [2], homicides either involving alcohol/drugs and/or by perpetrators who have a history of alcohol/drug problems were more likely to involve males who were strangers to each other. Alcohol/drug perpetrators were also more likely to be unemployed and have certain types of psychiatric disorders. Thus, the pattern that emerges is one of marginalization—that is, those committing alcohol or drug-related homicides are more marginalized from mainstream society (i.e. unemployed, unmarried, serious psychiatric disorder, conflicts with strangers), and this is especially true for drug-related homicides. Although homicides are generally often the result of escalation of private conflicts between friends, family and spouses, this appears to be less the case for alcohol- and drug-related homicides. Therefore, there may be greater opportunities for prevention of alcohol/drug related violence by focusing on where, when and under what circumstances alcohol- and drug-related crimes are committed using a situational crime prevention approach [3]. Another notable factor that emerges from this research is the high rate of previous involvement with treatment or legal systems (i.e. previous convictions for violence and contact with services) among alcohol/drug offenders. This finding highlights the need for the development of more effective rehabilitation programs for alcohol/drug offenders to prevent re-offending. In addition to addressing alcohol and drug misuse and the contribution of alcohol/drugs to violent behavior, such programs might include a greater focus on the routine activities of violent offenders who have alcohol/drug problems [4], focusing especially on identifying and changing the day-to-day activities of these rather marginalized offenders that put them at high risk of interactions that lead to homicide. This could also involve helping offenders to take on more mainstream and less marginalized roles in society. Although the high rate of violent crime associated with certain psychiatric disorders (e.g. personality disorder) has been recognized [5], few studies have examined both substance use/misuse and psychiatric disorders among offenders. While the authors say in the Discussion that these results suggest that a public health approach ‘would place a greater emphasis on substance misuse than on mental health’ (because of the higher rate of alcohol/drug involvement than of psychiatric disorders), the findings also highlight the importance of focusing research and interventions on the combined risk of alcohol/drug misuse and certain psychiatric disorders on future violent offending. This is an important area that has received relatively little attention in previous research. Finally, there are aspects of this paper that could benefit from clarification. The evaluation of the extent that alcohol and drugs contributed to the crime is an important strength of the paper; however, this is a complex topic with which researchers in the field have grappled for some time (see overview by Dingwall [2, chapter 3] and Pernanen & Brochu [6]). The paper could have contributed more to our understanding of this issue by describing their criteria for judging the extent of contribution of alcohol and drugs, the interrater reliability of these judgements and the proportion of offenders who had used these substances at the time but for whom alcohol-drugs were judged as not contributing to their crimes. Information on the specific drugs used at the time and the proportion judged as contributing would also have been extremely useful. Figure 1 describes general drug use among the offenders, but an entirely different drug profile might emerge in terms of drugs contributing to violence.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,019
score de la tête « metaresearch » (Gemma)0,041
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,100

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0190,041
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0040,003
Bibliométrie0,0080,006
Études des sciences et des technologies0,0030,005
Communication savante0,0060,012
Science ouverte0,0050,003
Intégrité de la recherche0,0140,013
Charge utile insuffisante (le modèle a refusé de juger)0,0080,002

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,318
Écart entre enseignants0,286 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2006
Routes d'admission1
Résumé présentoui

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