Maximising existing assets
The potential of Bayesian hierarchical approaches to improve the risk assessment process in youth justice
Bibliographic Data
| ID | 6418463 |
|---|---|
| Authors | Helen Hodges (0000-0001-7118-4490), Ernest V E Hodges (0000-0002-2282-7899) |
| Year | 2025 |
| Volume | 177 |
| Pages | 108502-108502 |
| Publication date | 2025-07-26 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Children and Youth Services Review (JOURNAL) |
| Journal identifiers | ISSN: 0190-7409 • E-ISSN: 1873-7765 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.childyouth.2025.108502 |
| OpenAlex | W4412672877 |
| Language | EN |
| References cited | 26 |
Bayesian approaches provide a fresh lens to consider the interaction between various characteristics and circumstances of children who have offended, and their likelihood of committing further offences over time. This has been done here by revisiting data captured as part of the risk assessment process used until the mid-2010s across England and Wales to consider how the probability of further offending behaviour differs depending upon whether the child has a prior history of offending or not. Whilst Asset has now been replaced by AssetPlus, the structure of the earlier risk assessment tool lends itself to mimicking rapid changes in the lives of some children and the evolving nature of youth offending. This has been done by treating the assessments conducted with 87 children in the formal youth justice system in a single Welsh local authority area whose supervision started in either 2012/13 or 2023/14 as a longitudinal dataset. The likelihood of further offending behaviour at different time points has then been estimated using additive binary logistical regression models based upon practitioner ratings for 12 domains of ‘risk’, along with a range of time-varying and non-time varying variables reflecting facets of their criminal career. This study demonstrates the utility of conducting the analysis in a Bayesian framework. Notably it highlights the potential to incorporate new ideas be they emerging theoretical perspectives, interventions or additional variables into existing models to increase understandings of the complex relationship between ‘risk’ and ‘protective’ factors for different subgroups
Actuarial science · Bayesian probability · Business · Economic Justice · Political science · Process (computing · Risk analysis (engineering · Computer Science · Crime Patterns and Interventions · Law · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence
Bayesian Cognitive Modeling
MCMC Methods for Multi-Response Generalized Linear Mixed Models
Risk assessment for juvenile justice
Bayes Factors
Bayesian Measures of Model Complexity and Fit
Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables
Understanding How Offending Prevalence and Frequency Change with Age in the Cambridge Study in Delinquent Development Using Bayesian Statistical Models
Does AssetPlus facilitate effective assessment of children within the youth justice system
Effective Practice in Youth Justice
What Works in Offender Rehabilitation
Youth crime and justice
Becoming criminal
Understanding Youth Offending
Are There Risks with Risk Assessment? A Study of the Predictive Accuracy of the Youth Level of Service-Case Management Inventory with Young Offenders in Scotland
Explaining and Preventing Crime
Multilevel Modeling in Plain Language
| Citation velocity | historical |
|---|---|
| Highly cited | No |