Evidence map›Paper›PMID 40114337›Full record

Observational studyJournal of intellectual disability research : JIDR2025

Predictive Validity of the Brøset Violence Checklist in a Secured Institution for Offenders With Intellectual Disabilities.

Jacob Hvidhjelm, Søren Holst

Abstract readObservational Study
In one paragraph

Observational study in Journal of intellectual disability research : JIDR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Observational
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Jacob HvidhjelmPsychiatric Research Unit, Mental Health Services, Region Zealand, Denmark.ORCID 0000-0002-9495-9786
Søren HolstDepartment of Sociology and Social Work, Aalborg University, Aalborg, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe integration of systematic risk assessments into care settings for offenders with intellectual disabilities (IDs) is a growing priority, yet evidence on the validity of existing tools in this population remains limited. The Brøset Violence Checklist (BVC) is widely used to assess short-term violence risk, but its predictive accuracy in individuals with IDs remains uncertain. This study investigates the predictive validity of the BVC in a specialized institutional context for offenders with IDs, focusing on its performance across different shifts and demographic subgroups.

methodA retrospective observational design was used to analyse 153 262 BVC assessments and 1325 documented severe violent incidents over 2.5 years in a secured Danish institution for offenders with IDs. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated across different BVC thresholds. Generalized linear mixed models (GLMMs) were applied to assess the influence of sex and time of day on predictive performance.

resultsThe BVC demonstrated high specificity and NPV across all shifts, confirming its utility in identifying low-risk scenarios. However, sensitivity and PPV were limited, particularly during night shifts and at higher score thresholds. Predictive accuracy was highest during evening shifts, aligning with periods of increased staff-resident interactions. GLMM analyses indicated that the relationship between BVC scores and violence risk was moderated by sex and time of day, with female residents and low-activity periods presenting unique challenges to prediction.

conclusionsThe findings underscore the need for population-specific adaptations to the BVC, particularly to address contextual and demographic factors influencing aggression in ID populations. Recommendations include supplemental assessment strategies for low-activity shifts, sex-specific behavioural indicators and the development of tailored tools for ID care settings. This study advances the understanding of violence risk dynamics in ID populations and informs interventions to enhance safety for residents and staff.

Indexed as

ChecklistCriminalsIntellectual DisabilityViolenceAdolescentAdultDenmarkFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesRisk AssessmentYoung AdultBrøset violence checklistintellectual disabilitiesoffender management and violence predictionrisk assessmentsecured institutions

Identifiers

PMID40114337
PMCPMC12051222

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.