A Validation of the Dynamic Risk Assessment for Offender Re-entry (DRAOR) for use with Offenders with Mental Disorder
Notice bibliographique
Résumé
Persons with mental disorders face widespread challenges in their lives, including disproportionate involvement in the criminal justice system.As there has been ongoing scholarly debate regarding relevant criminal risk factors for offenders with mental disorders, better understanding their experiences with the criminal justice system is essential to ensure they are being managed appropriately.The goal of the current doctoral research was to validate the Dynamic Risk Assessment for Offender Re-entry (DRAOR) for use with offenders with a mental disorder.A sample of 961 parolees in the state of Iowa (49.7% being diagnosed with a mental disorder) was used to achieve this goal.Findings showed that, while offenders with a mental disorder were assessed as having higher dynamic risk and lower protective factors, they were equally likely to recidivate compared to those with no mental disorder.While the DRAOR had utility with offenders who were not diagnosed with a mental disorder, results were less positive for those with a diagnosis.Discrimination analyses found that the DRAOR was only able to weakly discriminate between those who did or did not violate the conditions of their release, while calibration analyses found that the DRAOR may be under-classifying lowerscoring offenders with a mental disorder and over-classifying higher-scoring offenders with a mental disorder.The consideration of current mental health-related problems augmented the prediction of technical violations over DRAOR assessments for offenders with mental disorder, pointing to the possibility that there may be other factors relevant to risk prediction for this sub-population.Analyses focused on assessments over time found that, regardless of the presence of a mental disorder, offenders' levels of dynamic risk, but not protective factors, changed over multiple assessments.Subsequent analysis found iii DRAOR VALIDATION FOR OFFENDERS WITH MENTAL DISORDER that while DRAOR change scores significantly predicted future technical violations, they did not predict new charges.Overall, these findings point to the need for parole officers to exercise caution with using the DRAOR with clients who have a diagnosed mental disorder.Further research is needed to better understand the underlying reasons why the DRAOR does not work as well with offenders with mental disorders compared to those without mental disorders.iv DRAOR VALIDATION FOR OFFENDERS WITH MENTAL DISORDER Acknowledgements I would like to express my deepest thanks to my supervisor, Dr. Ralph Serin.Your enthusiasm and support over the past eight years of working together has been essential in developing my skills as a researcher.I am so grateful that you took me on.I would also like to
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 | 0,018 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
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 ».