An initial validation of a new measure of client change in a correctional sample
Bibliographic record
Abstract
Ideally, when an individual enters the Criminal Justice System there is the goal and expectation that they will, over time and through intervention, change from the individual who perpetrated the crime(s) toward adopting a non-criminal identity. However, there are currently few measures of change with established validity for predicting post-program or post-release outcomes with correctional samples. The Client Change Scale [CCS; Serin & Lloyd, Citation2018] was developed to address this gap to provide an empirically grounded systematic measure of factors relevant for change. This study was the first to assess the psychometric properties of the CCS with post-release outcomes and to predict recidivism outcomes with a Canadian release cohort using archival data. The sample included 390 adult males under community supervision by the Correctional Service of Canada (CSC) between 2015 and 2017. Overall, the CCS reflected acceptable psychometric properties. Additionally, an exploratory factor analysis revealed that all 16 items of the CCS should be retained and provided evidence for separate but correlated consideration of internal and external change factors. These initial findings suggest the CCS has the potential to improve decision-making throughout an individual’s time in the criminal justice system by utilizing a person-centered approach in support of improving client success and subsequent public safety.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".