Are Core Correctional Practice Strategies Associated with Positive Change Among Clients on Probation Irrespective of Gender, Indigeneity, or Mental Health and Substance Misuse?
Bibliographic record
Abstract
The intersection of mental health, substance use, and the criminal justice system presents significant challenges for probation and parole officers (PPOs), particularly when supervising justice-impacted persons (JIPs) with complex needs. Grounded in the risk-need-responsivity (RNR) model (Andrews et al., 1990), this research explores the relationship between core correctional strategies (e.g., teaching clients to reframe thinking styles) and client outcomes while on probation (e.g., less substance use, employment stability) and whether gender and Indigeneity moderate these effects. The study also explores if the presence of acute mental health crises and substance misuse influences the relationship between probation and parole officers’ use of Core Correctional Practice (CCP) strategies and client outcomes. The study analyzes 193 probation case notes following the introduction of the Ontario Solicitor General’s Made-in-Ontario Core Correctional Practices (CCP) Model of Community Supervision. A subset of six items from the Client Change Scale (CCS, Serin & Lloyd, 2018) is used to assess behavioural change across key life domains (e.g., employment, substance use, program engagement). Findings will inform evidence-based training enhancements and policy reforms to improve probation outcomes for diverse populations. The opinions and views expressed in this report reflect those of the author and not the Ontario Ministry of the Solicitor General. This project was done with the support of the Ontario Ministry of the Solicitor General.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".