The profile of people entering the ‘EQUIPS’ offender treatment programs in New South Wales’
Bibliographic record
Abstract
New South Wales has the largest population of incarcerated people in Australia, with increasing levels of community supervision. Corrective Services NSW offers eligible people the EQUIPS suite of offender treatment programs, which follow the Risk-Need-Responsivity model of offender rehabilitation. Referrals to the programs are also targeted to meet the specific reoffending needs of individuals, including EQUIPS Foundation, Aggression, Addiction and Domestic Abuse. This study examined the profile of people targeted for treatment in NSW by examining demographic, sentencing and criminogenic characteristics within a cohort of 18,963 individuals allocated to attend EQUIPS programs in custody and in the community between 2015 and 2018. Most individuals allocated to EQUIPS programs (80%) had a history of criminal justice system involvement, were male, with low education and most often from major cities or inner regional areas. Around a third were Aboriginal and/or Torres Strait Islander. Less than half of those referred to EQUIPS participated in at least one treatment session and only one quarter completed the course of treatment. Recommendations for improved program delivery include: 1) more timely risk assessment and allocation to programs during individual’s sentences; and 2) enhancing equitable allocation between custodial and community settings based on individual risk and the types of programs available.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".