The key role of base rates: systematic review and meta-analysis of the predictive value of four risk assessment instruments
Notice bibliographique
Résumé
AIMS OF THE STUDY: Many countries have seen a decline in recidivism rates over the past decades. These base rates are pertinent information for assessing the recidivism risk of offenders. They provide a foundation for clinical assessment and an empirical basis for risk assessment instrument norms, which inform expected recidivism rates. The present study explored the extent to which base rates influence the validity of risk assessment instruments. METHODS: We systematically reviewed the available evidence on the discrimination ability of four well-established risk assessment instruments used to estimate the probability of recidivism for general (Level of Service Inventory-Revised [LSI-R]), violent (Violence Risk Appraisal Guide [VRAG]), sexual (Static-99R), and intimate partner violent offences (Ontario Domestic Assault Risk Assessment [ODARA]). We conducted a bivariate logit-normal random effects meta-analysis of sensitivity and false positive rates and modelled the positive and negative predictive values. We used base rates as reported in (a) the construction samples of each risk assessment instrument and (b) recent official statistics and peer-reviewed articles for different offence categories and countries. To assess the risk of bias, we used the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. RESULTS: We screened 644 studies and subsequently analysed 102, of which 96 were included in the systematic review and 24 in the meta-analyses. Discrimination was comparable for all four instruments (median area under the curve = 0.68-0.71). The information needed to calculate summary statistics of sensitivity and false positive rate was often not reported, and a risk of bias may be present in up to half of the studies. The largest summary sensitivity and false positive rate were estimated for the ODARA, followed by the LSI-R, the VRAG, and the Static-99R. If base rates are low, positive predictive values tend to be relatively low, while negative predictive values are higher: positive predictive value = 0.032-0.133 and negative predictive value = 0.985-0.989 for sexual offences; positive predictive value = 188-0.281 and negative predictive value = 0.884-0.964 for intimate partner violence; positive predictive value = 0.218-0.241 and negative predictive value = 0.907-0.942 for violent offences; positive predictive value = 0.335-0.377 and negative predictive value = 0.809-0.810 for general offences. CONCLUSIONS: When interpreting the results of individual risk assessments, it is not sufficient to provide the discrimination of the instrument; the risk statement must also address the positive predictive value and discuss its implications for the specific case. As recidivism rates are neither stable over time nor uniform across countries or samples, the primary interpretation of risk assessment instruments should rely on the percentile rank. Expected recidivism rates should be interpreted with caution. However, our results are drawn from a limited database, as studies not reporting sufficient information were excluded from analyses and it was only possible to identify current base rates for modelling positive and negative predictive values for certain countries. International standards for consistently collecting and reporting base rates are important to better identify crime trends. Future research on the validity of risk assessment instruments should follow rigorous reporting standards.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,010 | 0,003 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».