Treatment Readiness and Engagement in a Sample of Male Justice System-Involved Youth
Notice bibliographique
Résumé
In the context of rehabilitative intervention for criminal justice system-impacted individuals, treatment readiness is defined as “the presence of characteristics (states or dispositions) within either the client or the therapeutic situation, which are likely to promote engagement in therapy and which, thereby, are likely to enhance therapeutic change” (Ward et al., 2004, p.650). Within the Risk-Need-Responsivity (RNR) framework, it has been conceptualized as a specific responsivity factor impacting an individual’s ability to successfully engage in services aimed at rehabilitation. However, the construct is understudied, particularly in the context of youth justice. There is currently no validated measure of treatment readiness for justice system-involved youth. This dissertation consists of two studies aimed at improving understanding and assessment of the construct in the population of justice system-involved youth. In the first study, I examined the psychometric properties of the Corrections Victoria Treatment Readiness Questionnaire (CVTRQ; Casey et al., 2007)), a 20-item self-report measure used to assess treatment readiness in justice system-involved individuals. In a sample of 274 male justice system-involved youth (aged 13-18), the internal consistency of the tool as a whole was ‘good’ (α=.80) but the four-factor structure suggested by the tool’s developers did not hold in a Confirmatory Factor Analysis. Construct validity was demonstrated via positive correlations with three measures tapping into similar constructs while discriminant validity was not supported. In terms of predictive validity, contrary to predictions, total scores did not predict attendance in probation services (a behavioral measure of engagement) or recidivism. In Study 2, I used a subsample of 149 male justice system-involved youth from Study 1 to explore relationships between readiness, engagement in services, and recidivism in order to improve our ability to determine youth who may struggle to engage and to better understand the role of engagement in outcomes. First, I examined relationships between empirically-supported predictors of engagement and service engagement. Next, I examined whether treatment readiness and willingness to participate in services predicted engagement in services.. I then used linear regression to determine whether engagement in services predicted recidivism and hierarchical logistic regression to determine whether it did so over and above recidivism risk and whether whether engagement in services moderated the relationship between risk and recidivism;. Taken together, study findings support that initial motivation to participate predicts subsequent engagement and suggest that service engagement is an important treatment target. Overall, results suggest that the CVTRQ may not be a robust measure of treatment readiness in justice system-involved youth. Results provide preliminary support for the use of two items on the YLS/CMI Attitudes/Orientation domain as a simplified indicator, but the development of a tool that assesses readiness in justice system-involved youth remains a research goal. Further research exploring engagement in different contexts is also warranted.
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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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».