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Enregistrement W2083223265 · doi:10.3310/hta17500

A systematic review of risk assessment strategies for populations at high risk of engaging in violent behaviour: update 2002–8

2013· review· en· W2083223265 sur OpenAlexaboutno aff
Richard Whittington, JC Hockenhull, James McGuire, Maria Leitner, W Barr, Mary Gemma Cherry, Rachel Flentje, Beverley Quinn, Y Dündar, Rumona Dickson

Notice bibliographique

RevueHealth Technology Assessment · 2013
Typereview
Langueen
DomainePsychology
ThématiquePsychopathy, Forensic Psychiatry, Sexual Offending
Établissements canadiensnon disponible
Organismes subventionnairesResearch for Patient Benefit ProgrammeDepartment of Health and Social CareNational Institute for Health and Care Research
Mots-clésPsycINFOCochrane LibraryMEDLINEMedicineMental healthPoison controlPsychologyClinical psychologyPsychiatryMeta-analysisEnvironmental health

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: This review systematically examines the research literature published in the period 2002-8 on structured violence risk assessment instruments designed for use in mental health services or the criminal justice system. It adopted much broader inclusion criteria than previous reviews in the same area in order to capture and summarise data on the widest possible range of available instruments. OBJECTIVES: To address two questions: (1) what study characteristics are associated with a risk assessment instrument score being significantly associated with a violent outcome? and (2) which risk assessment instruments have the highest level of predictive validity for a violent outcome? DATA SOURCES: Nineteen bibliographic databases were searched from January 2002 to April 2008, including PsycINFO, MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Allied and Complementary Medicine Database, British Nursing Index, International Bibliography of the Social Sciences, Education Resources Information Centre, The Cochrane Library and Web of Knowledge. REVIEW METHODS: Inclusion criteria for studies were (1) evaluation of a structured risk tool; (2) outcome measure of interpersonal violence; (3) participants aged 17 years or over; and (4) participants with a mental disorder and/or at least one offence and/or at least one indictable offence. A series of bivariate analyses using either a chi-squared test or Spearman's rank-order correlation were conducted to explore associations between study characteristics and outcomes. Data from a subset of studies reporting area under the curve (AUC) analysis were combined to provide estimates of mean validity. RESULTS: For the overall set of included studies (n = 959), over three-quarters (77%) were conducted in the USA, Canada or the UK. Two-thirds of all studies were conducted with offenders who had either no formal mental health diagnosis (43%) or forensic samples with a formal diagnosis (25%). The Psychopathy Checklist-Revised was tested in the largest number of studies (n = 192). Most studies (78%) reported a statistically significant (p < 0.05) relationship between the instrument score and a violent outcome. Prospective data collection (chi-squared = 4.4, p = 0.035), number of people recruited (U = 27.8, p = 0.012) and number of participants at end point (U = 26.9, p = 0.04) were significantly associated with predictive validity. For those instruments tested in five or more studies reporting AUC values, the General Statistical Information on Recidivism instrument had the highest mean AUC (0.73). LIMITATIONS: Agreement between pairs of reviewers in the initial pilot exercises was good but less than perfect, so discrepancies may be present given the complexity and subjectivity of some aspects of violence research. Only five of the seven calendar years (2003-7) are completely covered, with partial coverage of 2002 and 2008. There is no weighting for sample or effect sizes when results from studies are aggregated. CONCLUSIONS: A very large number of studies examining the relationship between a structured instrument and a violent outcome were published in this relatively short 7-year period. The general quality of the literature is weak in places (e.g. over-reliance on cross-sectional designs) and a vast range of distinct instruments have been tested to varying degrees. However, there is evidence of some convergence around a small number of high-performing instruments and identification of the components of a high-quality evaluation approach, including AUC analysis. The upper limits (AUC ≥ 0.85) of instrument-based prediction have probably been achieved and are unlikely to be exceeded using instruments alone. FUNDING: The National Institute for Health Research Health Technology Assessment and Research for Patient Benefit programmes.

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)0,018
score de la tête « metaresearch » (Gemma)0,080
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,029
Score d'incertitude au seuil0,093

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0180,080
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0090,008
Bibliométrie0,0290,024
Études des sciences et des technologies0,0010,001
Communication savante0,0030,004
Science ouverte0,0030,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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 Opus0,070
Tête enseignante GPT0,466
Écart entre enseignants0,396 · 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'étudeRevue systématique
Domainenon disponible
GenreSynthèse

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

En bref

Citations65
Publié2013
Routes d'admission1
Résumé présentoui

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