Treatment Readiness and Engagement in a Sample of Male Justice System-Involved Youth
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".