Interventions for Adult Offenders With Serious Mental Illness [Internet]
Notice bibliographique
Résumé
Objective To comprehensively review the evidence for treatments for offenders with serious mental illness (i.e., schizophrenia, schizoaffective disorder, bipolar disorder, or major depression) in jail, prison, or forensic hospital, or transitioning from any of these settings to the community (e.g., home, halfway house). Data sources We searched 12 internal and external databases including MEDLINE®, PreMEDLINE®, and Embase® for the time period January 1, 1990, through August 20, 2012. Review methods We refined the topic, Key Questions, and protocol with experts in the field and determined the study inclusion criteria and risk-of-bias items a priori. Abstract and full-text review and the risk-of-bias assessment were done in duplicate. A second reviewer verified data extraction. Extracted study information included study design, patient enrollment and baseline characteristics, risk-of-bias items, and outcome data. Because of the nature of the available evidence, we chose to perform a qualitative synthesis rather than meta-analysis. We graded the strength of evidence for each treatment comparison and outcome based on the size, risk of bias, and results of the evidence base. We discussed applicability by focusing on the populations, interventions, and settings of the studies. Results We included 19 publications describing 16 comparative trials. The studies were conducted in the United States, Canada, United Kingdom, New Zealand, and Australia. The risk of bias for all reported outcomes was medium for 15 trials and low for 1 trial. For incarceration-based interventions, evidence of low strength favored antipsychotics other than clozapine over treatment with clozapine for improving psychiatric symptoms. For all other incarceration-based interventions assessed—other pharmacologic therapies, cognitive therapy, and modified therapeutic community—evidence was insufficient to draw any conclusions. For individuals transitioning from the incarceration setting to the community, evidence of low strength supported discharge planning with benefit-application assistance and integrated dual disorder treatment compared with standard of care for increasing mental health service use and/or reducing psychiatric hospitalizations. Evidence was insufficient for comparing interventions administered by a forensic specialist with interventions administered by mental health professionals and for comparing interpersonal therapy with psychoeducation for offenders transitioning from incarceration to the community. More comparative trials are needed to increase our confidence in the findings for which the strength of evidence is low and to address the questions for which the evidence was insufficient. Conclusions We identified some promising treatments for individuals with serious mental illness during incarceration or during transition from incarceration to community settings. Treatment with antipsychotics other than clozapine appears to improve psychiatric symptoms more than clozapine in an incarceration setting. Two interventions, discharge planning with Medicaid-application assistance and integrated dual disorder treatment programs, appear to be effective interventions for seriously mentally ill offenders transitioning back to the community. The applicability of our findings may be limited to the populations and settings in the included studies.
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 | 0,006 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 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 ».