An Examination of the Professional Override in the Level of Service Inventory-Ontario Revision (LSI-OR)
Notice bibliographique
Résumé
Despite the overwhelming amount of research conducted on forensic risk assessments in the last twenty years there has been a distinct lack of information on the use of the professional override to adjust actuarial scores. The current study was designed to fill the gap in the research literature examining the effects from using the professional override in the Level of Service Inventory – Ontario Revision (LSI-OR). While there has been recent research conducted indicating that overrides or adjusted actuarial risk assessments are not as accurate as purely actuarial methods (Gore, 2007; Hanson et al., 2007; Hogg, 2011; Wormith, Hogg, & Guzzo, 2012) there is a lack of research conducted solely on the use of professional overrides in forensic risk assessment. This study analysed data from 40,539 provincial offenders in Ontario, Canada. The sample was primarily male (83.9%), White (63.0%), and was comprised of violent (53.0%), sexual (3.3%), and non-violent offenders (43.7%). Predictive validity analyses were conducted to determine the effects of the override for the total sample and then stratified by gender and ethnicity. Special attention was paid to the effects of the override compared between violent, sexual, and non-violent offenders. Results showed that the General Risk/Need score was most strongly correlated with non-violent recidivism over violent and sexual recidivism and that the General Risk/Need was significantly more correlated with non-violent recidivism for female offenders compared to male offenders. Correlation analyses showed that the initial risk levels appeared to be better predictors of general, violent, and non-violent recidivism whereas the final risk levels appeared to be better predictors of sexual recidivism in some cases. For violent and sexual offenders, the initial risk levels were significantly stronger predictors of general, violent, and non-violent recidivism than the final risk levels yet the final risk levels were non-significantly stronger predictors of sexual recidivism. There were no significant differences between the initial and final risk levels’ prediction estimates of the recidivism outcomes for non-violent offenders. Further, there were many more overrides used to increase risk levels than to decrease risk levels overall; sexual offenders had more overrides used to increase risk levels than violent and non-violent offenders combined. Risk level matrices indicated that there were many discrepancies between the number of offenders overridden and their corresponding recidivism rates. Regression analyses indicated additional discrepancies between the significant predictors of recidivism and the significant predictors of the override. Though there were certain methodological limitations to the current study the results still provide important information on the use of the override in a sample of male and female Ontario offenders. The results showed that the override resulted in decreased predictive validity of multiple recidivism outcomes. The conflicting information between the prediction of sexual recidivism and general, violent, or non-violent recidivism prevents a clear message being drawn from this study, yet the equivocal results provide further doubt and criticism of the use of adjusted actuarial practices in forensic risk assessment. Training assessors for how to use the override and examinations of the effects of the override for various offender groups must be improved and more frequently monitored. Further research should also focus on the reasons why overrides are used and if there are any biases concerning certain offender types. Misuse of the override has far-reaching ethical and legal implications that must be limited to ensure the future of forensic risk assessment is as accurate and appropriate as possible.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».