First Nations Peoples in the forensic mental health system in New South Wales: Characteristics and rates of criminal charges post-release
Bibliographic record
Abstract
BACKGROUND: It is well established that First Nations Peoples in Australia are overrepresented within the criminal justice system. However, First Nations Peoples appear to be comparatively underrepresented in the forensic mental health system, and little is known about their outcomes once released from secure care. OBJECTIVE: To compare the characteristics and rates of repeat criminal justice contact for a criminal charge of First Nations and non-First Nations forensic patients in New South Wales. METHODS: Data on the sample were extracted from the New South Wales Mental Health Review Tribunal paper and electronic files matched to the Bureau of Crime Statistics and Research Reoffending Database. Characteristics of First Nations and non-First Nations patients were compared using univariate logistic regression analysis. Univariate and multivariate Cox proportional hazard regression was used to determine predictors of post-release criminal charges. RESULTS: < 0.01). CONCLUSION: The findings of this study confirm that First Nations forensic patients have distinct and complex needs that are apparent at entry to the forensic mental health system and that their poorer criminal justice contact rates following release from secure care indicate that these needs are not being adequately met either during treatment or once in the community. Responses to these study findings must consider the complex and continuing impact of colonisation on First Nations Peoples, as well as the need for solutions to be culturally safe.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".